A summer French-themed bridal shoot with the seasoned and versatile wedding photographer esp.LOVE was the perfect excuse we needed to pack our bags and fly to France for a few days.
The photo shoot provided a rare opportunity to mix business with pleasure by spending a few days exploring the Aquitaine region in the South West of France. This region presented a delicious three-in-one mix of countryside, city and coast and for three days, she would be our lady and we her suitor. Located close to the French Atlantic coast, Aquitaine's capital city Bordeaux is widely regarded as THE wine capital of the world. With such a reputation, nothing was going to stop us from 'dipping our toes' into the multiple treats on offer.
Landing at the Aéroport de Bordeaux-Mérignac mid-morning, we observed a heavily armed patrol of French Gendarmes. We weren't sure if this escalation of security was usual or a left-over from the deadly Charlie Hebdo shootings earlier in the year. Unperturbed, we collected our bags and headed to Cestas, a residential town about thirty minutes south west of Bordeaux where our hostess lived. Cestas turned out to be a perfect hub for exploring the region. Although we were looking forward to improving our spoken French, we didn't complain when our lovely hostess and collaborator volunteered to be our guide and translator.
HDYTI Tip: The best way to see this region is by car. During our visit, the roads were great to drive on. Apart from the expected rush-hour congestion, there was hardly any traffic to deal with.
Our Aquitaine adventure began with the first half of the bridal photo shoot. esp.LOVE (aka Mrs. HDYTI) had scheduled the shoot to take place at Le Petit Moulin, a delightful French villa which doubles as a hotel and luxury wedding venue. Located in Annepont, a small community about 1 hr and 15 minutes drive north west of Bordeaux, this picturesque creeper-covered property was surrounded by lush green countryside; secluded but close enough to the local village and the grand Chateau de la Roche Courbon to give visitors a feel for the culture and history of the local area.
Voted by Conde Nast Traveller as one of the top 25 villas in Europe in 2011, Le Petit Moulin (aka The Small Mill house) brought to life esp.LOVE's ideas for the perfect venue for an intimate and romantic French-themed wedding. As the Mrs HDYTI did her thing with her stunning muse, we explored every inch of this gorgeous property with its simple elegance and delicate touches.
Once a working mill, the property has been modernized without losing any of its authenticity. Textured oak wood floors are beautifully complemented by a tiny little stream running through its large dining room. Halfway into the shoot, we were treated to the most amazing lunch by our lovely hosts. Strawberries and smoked salmon with the backdrop of the beautiful French countryside to boot had never tasted so good.
The second half of the photo shoot featured a Moroccan-Bohemian theme. For this, we needed some coastline, a secluded beach and a perfect sunset. Our resourceful collaborator had chosen the Plage du Platin beach in the beautiful coastal town of Saint-Palais-sur-Mer (Palace on the sea). With the sun starting to lose its intensity, we needed to move quickly and so under the direction of the dexterous esp.LOVE, we said goodbye to our hosts at Le Petit Moulin and headed towards the Aquitaine coastline.
Upon arrival in the near deserted town of Saint-Palais-sur-Mer, we made our way to water front and set up our gear just as the sun began to hang low over the horizon. Soaking up the sea breeze seasoned with perfumes from scented candles burning in the evening sunset, we wrapped up the photo shoot with a group photo and called it a night.
Exhausted from the exertions of the previous day, we enjoyed a late start the next morning. Photo shoot done, it was time to begin exploring and so we made quick work of our simple but lovely French breakfast and hit the road. The day before had given us a brief glimpse of Aquitaine's beautiful Atlantic coastal line. Eager for more, we set out for Arcachon, another coastal town about 55 kilometers west of Bordeaux.
The scenery on the drive to Arcachon was enhanced by giant maritime pine trees standing to attention on either side of the highway as if in a parade. Historically, this region was made up of swampy land and remained largely uninhabited until the mid-1800s when aggressive reforestation began. The maritime pines were introduced to soak up the excess water and now, over a century later, add to the character of the region.
The Arcachon coast features great sea views, clean beaches, sand dunes and a bay area attractive for water sports. Historically popular as a bathing town and being less than an hour from Bordeaux, it remains a magnet for the locals. However, as we visited on a week day and just before the summer holiday rush, we enjoyed walking along a mostly deserted promenade and past lots of attractive but empty cafes and restaurants.
We left the promenade and headed into the town. The wide streets were lined on either side with buildings featuring a mix of different but authentic architectures ranging from the modern to the classical. We meandered our way along Boulevard Veyrier Montagnères and uphill to the expansive Parc Mauresque which afforded us with excellent views of Arcachon's red clay rooftops, the sparkling sea in the distance and blue skies all around.
Thanks to our light breakfast, the tell-tale signs of hunger soon began to nag and so we headed back towards the promenade in search of food. This being the South West of France we were spoiled for choice and eventually decided on a sea-front restaurant which offered an appetizing selection of seafood and meats. The food paired well with a chilled bottle of Tariquet wine. With the sun high in the sky, we settled in and allowed ourselves to be seduced by 'Lady Aquitaine'.
A vibrant city – Green is good for you!
After enjoying the enchanting Aquitaine coast, our next stop was the city of Bordeaux. Our visit coincided with the biennial Bordeaux River Festival taking place along the Garonne River. The Bordeaux harbour swarmed with sailing enthusiasts, traders, yachtsmen and entertainers all preparing for what is regarded as one of the world's top yacht races: the Solitaire du Figaro Eric Bompard Cachemire. The city's excitement was palpable.
Bordeaux is a modern European city where cycling is well integrated into the public transportation architecture. With dedicated, wide and well signposted cycle lanes, riding through Bordeaux with the wind in our faces seemed like an appealing option. We wanted to feel the pulse of the city and so we ditched the car opposite the Conservatoire de Bordeaux Jacques Thibaud, picked up city bicycles and headed into the heart of the city.
No visit to the city of Bordeaux is complete without seeing the iconic Miroir d'eau (Water Mirror), a reflecting pool of water opposite the Palais de la Bourse. Unfortunately our visions of 'walking on water' never happened as someone had turned off the water jets just as we made our quick pit-stop. The expected mirror-like shine was replaced by a blanket of dull grey granite. Disappointed not to get our photo opportunity, we carried on towards the Pont de Pierre Bridge, the iconic bridge connecting the city's East and West banks, and paused to get our bearings. Most of the action seemed to be concentrated east of the Garonne River and so we remained that side of the city.
Designated as a World Heritage site by UNESCO in 2007, the city of Bordeaux comes with a large number of well preserved 18th century monuments and architecture. Following a quick stop at the Monument aux Girondins, a very dramatic fountain sculpture, Mr. HDYTI was slightly disappointed to learn that it was built in commemoration of martyred political figures from the French Revolution rather than something more mythical or divine.
Although the city had its seedier sections, we had the overall impression of a modern city, proud of its history but not bound by it. We explored the rest of the city on foot: food, gardens and window shopping along Rue St Catherine, one of the longest dedicated shopping streets in Europe. We later moved away from the crowds into quieter side streets, checking out coffee shops which were altogether a more intimate experience.
Our friend had heard about a vegan restaurant somewhere in the city. Walking and cycling had worked up a huge appetite and so with only one working phone between us, we turned on Google Maps and began the hunt. We soon found The Smart Green Corner, a quirky but unpretentious restaurant, tucked away in an alley off Rue Georges Bonnac and a short walk from Parc Gambetta.
This no-frills eatery served an eye-catching and flavorful selection of vegan and gluten free food, enough to turn Mr. HDYTI who is a habitual carnivore into an honorary vegan…at least for the rest of that day. A little sign above the cash register read 'GREEN FOOD IS GOOD FOR YOU'. With food very sensibly priced and high on quality, we couldn't agree more!
There are two types of people who visit Bordeaux's famous wine region: those who can tell their Cabernet Sauvignon's from their Petit Verdot's just by sniffing the air and the rest of us who haven't a clue! To the uninitiated and inexperienced, delving into the intricate details of Bordeaux's left bank and right bank wine producers and its rivalry with other French wine growing regions like Burgundy can be daunting. We will be honest enough to admit we are in no way oenophiles and so were looking forward to receiving some education on the subject.
The next day, we headed 45 minutes north of the city to Bourg sur Gironde. Located on the right bank of the Gironde River, we were headed to the award winning Château de la Grave run by Philippe and Valérie Bassereau. On arrival, we were greeted by Monsieur Philippe and a very curious cat. He (not the cat) immediately began a tour of the winery.
A master of his craft, Monsieur Philippe explained the planting and harvesting process and the methods employed in producing the Bordeaux blend unique to Château de la Grave. He explained his process of leaving the ground to fallow for three years before replanting with the next generation of grapevines and how he tastes the wine everyday to get it just right. There are certainly worse jobs than that!
Although we didn't speak his language, his passion for his craft and his produce was infectious. He obviously seemed to know what he was talking about, and so we followed him, spell bound, to the wine tasting room of the château. The cat seemed rather keen to see what we were up to and followed us into the tasting room. While we tasted three of the château's wines, the cat soon got bored with our sniffing and swirling shenanigans, took up a vantage position where the sunlight came in through the window and proceeded to ignore us for the rest of the afternoon.
We needed no further prompting to pick up a few bottles of the château's 2012 Nectar de la Grave. Pleased with the bargain prices and somewhat better educated about wine, we drove off into the Bordeaux countryside with our prize.
HDYTI Tip: Philippe and Valérie Bassereau rent out holiday rooms at certain times of the year at Château de la Grave. To enquire, go to their contact page here or here. Monsieur Philippe was however very emphatic about not hosting weddings at the chateau though! There are also wine tours across Bordeaux that offer family friendly activities. While the adults indulge, kids can also enjoy various learning activities.
We had come to 'Lady Aquitaine' as suitors and we were leaving enchanted. However, she was not done with us yet. There was one more surprise in store for us. Famished after our tour to the winery, our thoughts turned again to food. Driving along a highway somewhere between Cote de Bourg and Bordeaux we spotted a sign that indicated a restaurant was just off the road. Not knowing what to expect, we veered off the highway and were immediately transported back in time. Following a windy country road, we soon arrived at Restaurant Aux Amis Reunis in Saint-Laurent-d'Arce. Located opposite an old church along a deserted village street, we walked in and were soon seated.
This was the sort of place which does not show up on review websites. The locals apparently keep this their own little secret but seemed happy to share it with us black strangers. The nondescript décor was overshadowed by a large group of men who appeared to be the village rugby or football team having lunch.
We placed our orders and Mr. HDYTI appeared very curious when his entrée of Chevre Chaud au Miel et Lardons (hot cheese and bacon) arrived. By the time his main plate of tender rabbit with Sauce Champignons et Lardons arrived, he was wearing a very wide grin. At the end of the meal, we were all in love; in love with the food, in love with each other, in love with Bordeaux. Soul food is the kind that seeks you out when you least expect it and leaves you wanting more! We had just found its Bordeaux incarnation!
With a refreshing combination of coast, countryside and city, Lady Aquitaine had proven that she had a lot to offer. We will certainly be back for more.
Have you been to Bordeaux? What are your favorite chateaux? Have you visited multiple wine regions? Please share your favorites and why. We would love to hear from you and perhaps check out your recommendations. Also watch this space for our 'education' on English wines….yes, the Brits also produce wine!


Dakkas woke to darkness and pain. He moved his head and a wave of nausea nearly made him pass out again. His shoulders ached; he tried to move them to relieve the strain and realized he was bound by his wrists, his arms taking the brunt of his weight, his feet barely touching the ground.
"Good. You're awake. I wouldn't want you to miss the entertainment I've arranged for you." The voice sounded familiar but the pounding in his head made it hard to hear anything other than the memory of Rebel's screams. He forced his eyes to focus but all he could see was a stone table lit by small guttering torches. He glanced up at his wrists: the ropes holding him were attached to rings set into stalactites.
oh no …. not a good situation to be in … you got me hooked, can't wait to read more!
Oh – you are being really horrible to these characters!
Dang Orcs. This isn't good.


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Caravan at border, Brookln shooting, Lucasville
Bob and Dan discuss Trump's latest craziness about the caravan of people at the Mexican border, the recent shooting of a black man in Brooklyn, and talk about some of the issues and events in the April Free Press, including a...
The Other Side of the News March 9 2018-Trump, Manafort, Connell and local news
The Other Side of the News March 2 2018-Trump, Russians, Nazi flyers in Clintonville
Billy Graham's death, the events in Syria, the Russian indictments and the corruption of Columbus City Council
Bob and Dan discuss current events including the school shooting and the fallout, Billy Graham's death, the events in Syria, the Russian indictments and the corruption of Columbus City Council
Trump's latest desire for a military parade, his immigration policies and about Edith Espinal
Bob talks about Trump's latest desire for a military parade, his immigration policies and about Edith Espinal, the Mexican woman currently in sanctuary in Columbus. The last 15 minutes are from a recent fundraiser for Edith.

redpajama_set_name: RedPajamaCommonCrawl


King honored Parker's original thought by breathing new life and meaning into his 19th-century words. There is no question of plagiarism; King made no secret of the source. As clergy, they also shared a religious basis for their social ideals.
King forcefully revived Parker's "arc" quotation several times in the 1960s. Years later, Obama loved the line so much that he chose it as one of five for the Oval Office rug — without reference to Parker.
Let's not allow the same omission in a place of remembrance that will be visited by countless Americans in the decades to come.
The Parker-King connection across generations and color lines should be celebrated as an example of how an unfinished life work can be carried on.
The night before he was assassinated in Memphis in April 1968, King said: "I've seen the Promised Land. I may not get there with you." Parker died in 1860 before seeing the dream of Emancipation come true.
Perhaps someday soon history's arc may bend and give Parker a little justice of his own.
Jamie Stiehm, a journalist, is writing a book on the life of Lucretia Mott, a 19th-century Quaker abolitionist and women's rights leader.


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Over 400 species of macromoths and 100 species of micromoths have been recorded at Treborth where a Robinson Light Trap has been operated on a nightly basis since 1989. In that time over a third of a million individual moths have been caught making it one of the most comprehensive recording efforts in the UK. The data have now been digitised by Pat Denne.
Some species are extremely abundant such as Large Yellow Underwing and Heart and Dart (up to 500 per night in July/Aug!) whereas the majority are trapped in much smaller numbers and not necessarily every year. Some species have increased dramatically since the nineties e.g. several lichen feeders such as Buff Footman and Dingy Footman. The appearance in 1996 and subsequent year on year increase in Blair's Shoulder-knot mimics the countrywide situation albeit some years behind sites further south. Lime Hawk Moth has likewise made a welcome first appearance in 2001 and is now established, though in small numbers. Black Arches has become regular in late summer and autumn but was previously unrecorded, until the early years of this century. August Thorn and Pretty Chalk Carpet have both become much more frequent since the eighties and nineties, the latter perhaps associated with the spread of its main food plant, Traveller's Joy (Clematis vitalba) which has spread up the driveway of the botanic garden and along the railway track.
On the negative side however a number of species have shown marked declines or even disappeared altogether. Garden Tiger is perhaps the best known in this unfortunate category mirroring a nationwide decline. The melanic form of the Peppered Moth has declined to the point of being virtually extinct.
Treborth is a generally a very good site for woodland and grassland species including some nationally scarce taxa such as Brussel's Lace and Blomer's Rivulet. Recent additions include Pauper's Pug in 2016.


The UGA Institute of Gerontology invites Master of Public Health (MPH) students to celebrate the last week of classes and come learn about careers in aging on Tues. Nov. 28 in Hudson Hall on the Health Sciences Campus (Parking will be free). Invitees includes UGA faculty focused on aging research from all across campus; members from the community that are actively aging; and individuals currently working in the aging field, including an MPH alumna to prove you can get a great job in aging with our Master of Public Health (MPH) degree in gerontology.
Come join us for some food and conversation about aging. Please RSVP to Kerstin Emerson at emerson@uga.edu.


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careers ·WMPW
The World's Most Powerful Women: December 6
BYClaire Zillman
December 6, 2016, 8:41 AM UTC
There's lots of speculation about who will replace Italian PM Matteo Renzi, who resigned yesterday after suffering a massive defeat in his effort to pass a referendum on constitutional reform. But we know this much: it's unlikely to be a woman.
As Fortune's Laura Cohn reports, Italy and Japan are the only Group of Seven nations that have never had a female leader or a leading female candidate for the country's top political job. It's somewhat surprising that Italy earns that classification since it has a decent record of female representation in Parliament. It ranks 42nd out of 193 countries in terms of women in the legislature, ahead of the U.S. at 99th and every other G7 nation except for Germany at No. 24.
But despite their presence in politics, female elected officials still struggle to be taken seriously. For example, in mayoral elections earlier this year, Silvio Berlusconi, the former prime minister and media tycoon, refused to support Giorgia Meloni—a leading rightwing candidate for Rome's mayor—because he said her pregnancy made her unfit for office.
Says Simona Aimar, an assistant professor at University College London: "Italian politics has not been structured for women, and biases and man-only camaraderie are rampant."
clairezillman
Keep it in the courtroom
Gina Miller, the investment manager who helped bring the landmark case challenging the U.K. government's authority to trigger Brexit, says she's received death and rape threats from Brexit supporters accusing her of thwarting the outcome of the June referendum. As the Supreme Court began hearing the case yesterday, Lord David Neuberger blasted those who'd attacked Miller: "Threatening and abusing people because they are exercising their fundamental right to go to court undermines the rule of law."
"7 months later, 1 woman standing."
Rome-based reporter Megan Williams made the astute observation yesterday that of the world leaders in this photo, only Angela Merkel is left standing.
A female brain trust
Seventeen Microsoft researchers—all of whom happen to be women—have issued their annual predictions for artificial intelligence advancements in the next decade. They hypothesize that deep learning will make computers intuitive and that virtual reality will become more ubiquitous. The tech giant focused on female researchers this year to stress that women are underrepresented among computer science graduates.
On-campus chasm
According to the latest data, of the 25 highest-paid private college presidents in the U.S. just three—University of Pennsylvania's Amy Gutmann, University of Miami's Donna Shalala (who retired last year), and Rensselaer Polytechnic Institute's Shirley Ann Jackson—are women. In the top 100 earners, there are 17 women.
Reflecting reality
I took a closer look at Donald Trump's new panel of business advisors. Of the 16 members, just two—IBM's Ginni Rometty and GM's Mary Barra—are women. Blackstone CEO Stephen Schwarzman, who assembled the panel, said party allegiances didn't factor into his selections, and perhaps, neither did diversity. But the sad fact is that women's underrepresentation on the panel reflects their larger struggle to reach parity in Corporate America.
An impending impeachment
There are reports today that South Korean President Park Geun-hye is working with her party to negotiate an agreement in which she would resign in April over an influence-peddling scandal that's prompted mass protests. The talks appear to be an attempt to preempt an impeachment vote set for Friday, but the balloting may still proceed.
Alienating an ally
The Philippines Vice President Leni Robredo resigned from President Rodrigo Duterte's cabinet yesterday and vowed to lead the opposition to his policies, such as his deadly war on drugs. Robredo will remain VP—a role she won in an election separate from Duterte's—but will leave her ministerial post after being told by text message to stay away from cabinet meetings.
Straits Times
Happy wife, happy...nation
With sky-high approval ratings, it's possible that Japanese PM Shinzo Abe could become complacent. His wife of 29 years, Akie Abe, is ensuring that doesn't happen. She's gained a reputation as "the household opposition," challenging Abe's positions on policies such as the Trans-Pacific Partnership trade pact. Her attitude flouts the traditional, deferential demeanor of Japan's first lady. "I want to pick up and pass on the views that don't get through to my husband or his circle," she says.
Oprah will conduct Michelle Obama's final White House interview
People are worried Donald Trump will reverse the plan to put Harriet Tubman on the $20 bill
Elizabeth Holmes' Theranos foresaw huge growth in revenue and profits
Researchers may have 'found' many of China's 30 million missing girls
100 years ago, women staged an all-female coup in a tiny Oregon town
Ivanka Trump had business at stake when she met Japan's prime minister
"I also believe that kindness is the cure to violence and hatred around the world."
--Singer Lady Gaga, talking about her PTSD to homeless LGBT teens.


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redpajama_set_name: RedPajamaGithub


Dress features shaped bodice with seam interest extending to raglan sleeves and softly bloused effect waistline. Short or elbow length sleeves.
Printed pattern pieces. Pattern is cut and complete. Comes with original mailing envelope and small newspaper ad from which the pattern was purchased - hand dated January 1963.


1924 – A German court sentenced Adolf Hitler to five years in jail for high treason after his abortive 1923 putsch; he was released on Dec. 20.
1976 – Max Ernst, German painter and sculptor and one of the founders of Dadaism, died.
1996 – The world's largest bank was created in Japan with the merger of Mitsubishi Bank and the Bank of Tokyo.
1999 – In the first redrawing of Canada's provincial borders in 50 years, a vast new Arctic territory called Nunavut was created.
1999 – BP Amoco Plc agreed a $26.8 billion takeover of Atlantic Richfield Co of the United States to create the world's biggest non-OPEC oil producer.
2001 – Former Yugoslav president Slobodan Milosevic was arrested in Belgrade after a stand-off. He pleaded not guilty to charges of abuse of power and criminal conspiracy.
2001 – A U.S. Navy surveillance plane with 24 crew made an emergency landing on the Chinese island of Hainan after a mid-air collision with a Chinese F-8 fighter, sparking a diplomatic stand-off that was to last several weeks.
2005 – Harald Juhnke, one of Germany's best-loved stars of television, film and stage and hailed by many as the German Frank Sinatra, died aged 75.
2007 – French acrobatic basketball team "Crazy Dunkers" member Willy Martignon breaks world record for dunking from the longest distance.
2012 – "Karat chef" creates world's most expensive sushi.
2014 – General Motors CEO Mary Barra testifies on Capitol Hill on auto-related deaths.


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Watch News 13 This Weekend at …
A sign in front of the Food and Drug Administration building is seen on Dec. 10, 2020, in Silver Spring, Md. The FDA's tobacco division is plagued by a…
A sign in front of the Food and Drug Administration building is seen on Dec. 10, 2020, in Silver Spring, Md. The FDA's tobacco division is plagued by a lack of clear direction and priorities that have hampered its ability to regulate electronic cigarettes and other products under its oversight, according to a report released Monday, Dec. 19, 2022. (AP Photo/Manuel Balce Ceneta, File)
FDA grants fast-track review for over-the-counter overdose drug
by: Julia Mueller, The Hill via Nexstar Media Wire
(The Hill) – The Food and Drug Administration (FDA) is fast-tracking the review for a new opioid overdose reversal drug that would be available over the counter.
The nonprofit pharmaceutical company Harm Reduction Therapeutics on Monday said in a release the FDA had granted it priority review for a new drug application for RiVive, a naloxone nasal spray for emergency overdose treatment.
Naloxone is a drug administered to reverse the effects of a suspected opioid overdose. Under brand names like Narcan, naloxone is usually available without a prescription, but sold behind the counter.
Harm Reduction Therapeutics co-founder and CEO Michael Hufford said the drug application's progress indicates "the public health landscape is beginning to evolve" and that the low-cost, over-the-counter drug is "hopefully set to become a reality."
It will still be a while, though, before the FDA makes a call on RiVive, with an approval decision expected by the end of April 2023.
Anticipating a green light from the agency, the nonprofit says it's entered a commercial supply agreement and is preparing to launch the drug in the U.S. in 2024.
The FDA earlier this month also granted priority review for Emergent BioSolutions Inc., the maker of Narcan, for a two-dose naloxone nasal spray, with an approval date expected in March 2023.
Researchers and drug manufacturers are pushing for the drug to be accessible without a prescription and without talking to a pharmacist behind the counter as the country deals with a growing opioid epidemic.
Opioids are the leading driver of drug overdose deaths in the U.S., according to the CDC.
News of the latest push for fast-track approval was first reported by The Wall Street Journal.
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Reliance Jio in the past experimented a desktop version of Jio TV to allow users to enjoy live streams on a bigger screen.
Reliance Jio's JioTV is currently one of the most popular liked OTT applications in the market. The popularity of Reliance Jio makes it even more accessible by the users since the app is bundled in Reliance Jio's free portfolio of apps.
Reliance Jio always believe to be the frontrunner in the content building race, Reliance Jio has been making big bang investment in creating new and unique content from the makers to ensure that the customers gets only the best when it comes to experiencing video-on-demand service. Now, bringing a tiny but important feature for next generation users Reliance Jio has upgraded its JioTV application on its Android platform.
The company has brought picture-in-picture mode for JioTV. Notably, this new feature will allow the users of JioTV to continue with their work like texting, chatting, browsing, typing or anything else while continuing to watch the video, movie or TV show in the small hovering thumbnail box.
When you open the JioTV application page on Google Play Store, you will find that Reliance Jio has recently updated and listed the latest features of the app. On the top, we get to see the new feature addition mentioning of the picture-in-picture mode which will enable the users to continue their chatting, browsing or anything else while the video plays in the thumbnail even after exiting the application.
This way, users will be able to exit the JioTV app while the video continues to play in the small hovering screen box. However, JioTV has also observed that the picture-in-picture functionality will only work on smart phones with Android 8.0 and above. While you install the application, you also might have to allow the picture-in-picture functionality in the settings for the JioTV app. On doing that, you will be able to shrink the screen while playing a video next time to turn to picture-in-picture mode.
It was observed in the past few months back, probably in 2018, WhatsApp had also received a picture-in-picture update which helped the users to watch videos from Facebook, Twitter, Instagram and YouTube in picture-in-picture without needing to keep the chat window open. Users could keep chatting, while the video was playing.
Besides, the company has expanded its original channels to focus on Indian Premier League and General Elections 2019, both of which are underway in India. The catalogue of channels has also been increased with more regional channels as the consumption of vernacular media and rural population demand is rising, thereby, forcing production companies to launch regional channels and creating more local and regional content now more than ever.
The Android app on Google Play is rated 4.4 stars with over 2 million reviews and over 10 million downloads.


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You are here: Main » More on Workhorse, the company interested in GM Lordstown
More on Workhorse, the company interested in GM Lordstown
by Lynn Briggs	- at May 09, 2019
The discussions with Workhorse Group Inc., which makes plug-in pickups and all-electric delivery vans, and an affiliated new entity could bring "significant" production and assembly jobs to the Lordstown manufacturing complex, GM said in a statement on Wednesday, May 8.
DeWine said he was "trying to be realistic" about what might happen.
Should this sale finalize, the fate of the plant - widely thought to be in the hands of negotiators with the UAW and GM when they sit down later this year to hash out a new collective bargaining agreement - already will have been sealed and GM won't be part of its future. "That would be one of the goals of the company as they grow their business - to get a contract with the post office", DeWine said.
GM would not be involved with Workhorse's operations if the deal happens, said U.S. Rep. Timothy J. Ryan, D-Howland. Trump in another tweet added that GM will spend $700 million in 3 separate OH locations, creating 450 jobs.
Separately, GM and the largest union representing Canada's auto workers have reached a deal to partly rescue an Ontario car-making plant slated to close this year by turning it into a parts-making facility, the automaker said in a statement.
GM produced its last Chevrolet Cruze compact auto at the Lordstown factory in March and had said it didn't have any future product planned for the 53-year-old facility. Many will be forced to transfer in order to preserve seniority and pension eligibility, O'Hara said. Union workers were livid that they agreed to make $118 million a year in annual concessions to save the plant in mid 2017, only to have the company effectively threaten to close it down a year and a half later.
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"General Motors should assign a product to the Lordstown facility and continue operating it", Dittes said.
GM closed the Lordstown plant in March, laying off 1,400 workers. "Great news for Ohio", Trump tweeted.
Also, Workhorse and another company have partnered to bid on a contract for the U.S. Postal Service's "next-generation delivery vehicle".
Workhorse also announced plans a year ago to develop a hybrid-powered passenger drone, and has suggested it could build similar models for its package delivery customers.
The decisions came after GM faced months of criticism over its plan announced in November to close five North American plants and cut 15,000 jobs.
But the remainder of the 2,600 workers at the plant in Oshawa, near Toronto, are still scheduled to be laid off.
When GM announced the plant-closure, it said it would find other jobs for most of its 1,600 workers, though many were at other factories and would require relocation.
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text:
Assessment of Heavy Metal Distribution and Health Risk of Vegetable Crops Grown on Soils Amended with Municipal Solid Waste Compost for Sustainable Urban Agriculture
Analysis of the Formation Mechanism of Medium and Low-Temperature Geothermal Water in Wuhan Based on Hydrochemical Characteristics
A Comparison of Different Water Indices and Band Downscaling Methods for Water Bodies Mapping from Sentinel-2 Imagery at 10-M Resolution
Chang Huang
Xu, J.
Feng, M.
Sui, Y.
Yan, D.
Zhang, K.
Shi, K.
Identifying Alpine Lakes in the Eastern Himalayas Using Deep Learning
Jinhao Xu
1,2 ,
Min Feng
1,3,* ,
Yijie Sui
Dezhao Yan
Kuo Zhang
Kaidan Shi
National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Received: 21 November 2022 / Revised: 30 December 2022 / Accepted: 1 January 2023 / Published: 5 January 2023
(This article belongs to the Special Issue Inland Surface Water and Deep Learning)
Alpine lakes, which include glacial and nonglacial lakes, are widely distributed in high mountain areas and are sensitive to climate and environmental changes. Remote sensing is an effective tool for identifying alpine lakes over large regions, but in the case of small lakes, the complex terrain and extreme weather make their accurate identification extremely challenging. This paper presents an automated method for alpine lake identification developed by leveraging deep learning algorithms and multi-source high-resolution satellite data. The method is able to detect the outlines and types of alpine lakes from high-resolution optical and Synthetic Aperture Radar (SAR) satellite data. In this study, a total of 4584 alpine lakes (including 2795 glacial lakes) were identified in the Eastern Himalayas from Sentinel-1 and Sentinel-2 data acquired during 2016–2020. The average area of the lakes was 0.038 km2, and the average elevation was 4974 m. High accuracy was reported for the dataset for both segmentation (mean Intersection Over Union (MIoU) > 72%) and classification (Overall Accuracy, User's and Producer's Accuracies, and F1-Score are all higher than 85%). A higher accuracy was found for the combination of optical and SAR data than relying on single-sourced data, for which the MIoU increased by at least 12%, suggesting that the combination of optical and SAR data is critical for improving the identification of alpine lakes. The deep learning-based method demonstrated a significant improvement over traditional spectral extraction methods.
alpine lake; glacial lake; deep learning; satellite imagery; multi-source data
Alpine lakes are lakes located at high altitudes in mountainous zones [1] and include glacial and nonglacial lakes. Glacial lakes are water bodies influenced by the presence of glaciers. They include ice-contact lakes, which are next to glacier ice, and distal lakes, which are distant from the originating glaciers or ice sheets but are still influenced by them [2]. Glacier recession regulates the formation of and changes in glacial lakes, especially in glacier-rich regions such as the Himalayas [3,4,5]. These changes are often considered one of the most visible signs of global warming [6] and increase the risk of glacial lake outburst floods [7,8]. These outbursts can release a large mass of water and sediment in a short time [9], representing a serious hazard to downstream human life, property, and ecosystems [10].
Satellite data provide a more effective approach for surveying alpine lakes in large regions because alpine environments are remote and difficult to visit. However, visual interpretation has been the common approach for identifying alpine lakes in satellite images due to the lack of automated approaches for accurately recognizing small alpine lakes [11]. Because of the amount of labor and time involved, manual visual interpretation is not suitable for covering large areas.
Water sensitive indexes, such as the Normalized Difference Water Index (NDWI) [12], Modified Normalized Difference Water Index (MNDWI) [13], and Automated Water Extraction Index (AWEI) [14], have been shown to be effective for detecting water in multispectral satellite data. Water indexes have also been used for alpine lake identification in countries such as Nepal [15]. However, because of the complex terrain and weather conditions in the alpine environments, water indexes can result in considerable errors when used for alpine lake identification [16]. The combination of water indexes and other datasets has been found to be effective in improving the ability of alpine lake identification. For example, the topographic features derived from the digital elevation models (DEMs) were found helpful for separating mountain shadows from glacial lakes [17]. It is also common to combine water indexes and different segmentation methods to improve the accuracy of alpine lake identification. For example, Non-local Active Contour (NLAC) has been used to deal with regional image heterogeneity [13].
Cloud contamination can severely limit the use of optical images in mountainous regions for lake identification. Synthetic Aperture Radar (SAR) observations can penetrate clouds and are sensitive to water. Therefore, SAR observations have been adopted for water mapping and monitoring, including for alpine lakes [18,19]. Despite their all-weather condition capability [20], SAR observations also present some disadvantages for water detection, such as inherent topography-induced effects and speckle noise [19].
A detailed classification system of glacial lakes was proposed based on their formation mechanism, topographic features, and geographical location [21], including glacial erosion lakes, moraine-dammed lakes, ice-blocked lakes, supraglacial lakes, subglacial lakes, and other glacial lakes. Methods have been developed to identify alpine lakes and their types using satellite images by considering examining certain conditions such as distance to glaciers [22], minimum elevation [23], or whether the lakes are located in a glacial development area [24]. However, these simple conditions are usually insufficient for accurately identifying alpine lakes. Random forest or other machine learning algorithms have been used for establishing more sophisticated models to distinguish between glacial and nonglacial lakes by considering more features of lakes [25]. Glacial lakes are usually dammed by debris from glacial movement, and these dams are very fragile but distinguishable. Therefore, considering the structure and environment of an alpine lake could improve the ability to identify glacial or nonglacial lakes. However, these features usually only consider the water instead of the surrounding environment. Deep learning presents a stronger learning ability by considering more features of the targets and their environments, providing a promising approach for detecting alpine lakes and their types.
Deep learning algorithms, represented by Convolutional Neural Networks, have evolved rapidly in recent years. Compared to the pixel-based traditional remote sensing analytics, deep learning has demonstrated a strong spatial feature extraction ability by establishing relationships between pixels. The potential of deep learning in water identification, especially glacial lake identification, has been explored. Using UNet and very high-resolution satellite (VHRS) imagery, more than 5000 water bodies were identified in the Hindu Kush, Karakoram, and Himalayas (HKKH) regions, which is much higher than the number in existing inventories [26]. By improving and optimizing the UNet model, the established algorithms can extract glacial lakes more effectively [27,28,29]. However, UNet is an early deep learning network with limited performance. Advanced deep learning networks that have been developed based on more effective architectures have demonstrated improved identification accuracy compared with UNet [30,31]. Despite the ability of deep learning networks, limited sample data lead to inadequate model training and result in false positive errors. However, there are alpine lake datasets available that can be used as training data for deep learning networks. Therefore, the current focus of using deep learning to identify glacial lakes should take full advantage of the existing networks and data.
This paper presents an automatic method for the identification of glacial lakes based on deep learning combined with multi-source satellite data to overcome the limitations of field investigations and manual visual interpretation. In addition to detecting alpine lakes and their outlines, the method presented is capable of distinguishing differences between glacial and nonglacial lakes by extracting the characteristics of their surrounding environments. The advantages of multi-source satellite data (true color images, water indexes, SAR, and DEMs) were integrated to enhance the performance of the identification model. A deep learning label set of high quality was created containing typical glacial lakes and various other objects in the Eastern Himalayas. The presented method can distinguish glacial lakes and nonglacial lakes by their surrounding environment. Knowing the type of alpine lakes could greatly improve our knowledge of these ecosystems as well as provide critical information for assessing potential hazards created by these lakes.
2. Study Area
The Eastern Himalayas (85.2–89° E) is an east-west mountain range with an average elevation of 4774 m (Figure 1). A total of 2892 continental glaciers are distributed in the region, covering a total area of 3733 km2. The region has a tropical monsoon climate with an average annual temperature of −2.6 °C and annual precipitation of 1033 mm. Influenced by the Indian Ocean monsoon, the southern Eastern Himalayas has abundant rainfall and dense vegetation, whereas the northern part is desolate and dominated by an arid climate. The area is mostly covered by clouds throughout the year with only about 10% of clear days [32]. Most of the precipitation (>40%) is concentrated in the summer [33], which is the period of high geological disaster risks. Alpine lakes in the Himalayas have been growing rapidly amid climate change and glacier melting in recent decades [34], making the Eastern Himalayas one of the densest and most frequent areas of glacial lake outburst floods [35], with the highest death toll worldwide [36].
3.1. Data
The Sentinel series of satellites are developed by the European Space Agency (ESA). The Sentinel-1 series is a C-band radar imaging mission dedicated to land and ocean monitoring, with a maximum spatial resolution of roughly 5 m. The Sentinel-2 series is a multispectral high-resolution imaging mission for land monitoring with a maximum spatial resolution of 10 m.
The ALOS PALSAR radiometrically terrain corrected (RTC) Digital Elevation Model (DEM) dataset was developed by the Alaska Satellite Facility (ASF) to provide information about elevations at 12.5-m and 30-m resolutions in GeoTIFF raster data format. We used the 12.5-m resolution dataset to meet the spatial resolution of the Sentinel images. The dataset was downloaded from the ASF Distributed Active Records Center (https://asf.alaska.edu (accessed on 4 January 2023)).
3.2.1. Data Preprocessing
The Google Earth Engine (https://earthengine.google.com (accessed on 4 January 2023)) was used for removing cloud cover and synthesizing the Sentinel-1 and Sentinel-2 summer images covering the study area between 2016 and 2020. Multiple bands, i.e., Sentinel-1 (VV (10 m)) and Sentinel-2 (Red (10 m), Green (10 m), Blue (10 m), and shortwave infrared—SWIR (20 m)), were selected to characterize the alpine lakes and their environments. The Sentinel-2 SWIR band was resampled from 20 m to 10 m to match the spatial resolution of the other bands.
Four variables were derived from the bands to enhance the representation of water, terrain, and the environment (Table 1) for training and driving deep learning networks.
As one of the most effective water indexes, the MNDWI was calculated as:
MNDWI = Green − SWIR Green + SWIR
The ALOS PALSAR DEM dataset was also resampled to 10 m, and the Relief variable was derived from the DEM by calculating the difference between the maximum and minimum values within a 100 m × 100 m grid:
Relief = Grid max − Grid min
3.2.2. Visual Interpretation of Alpine Lakes
The purpose of visual analysis is to obtain data for training and validating the alpine lake identification and classification models. Three interpreters participated in the interpretation and labeled all of the lakes in the study area by visually examining the high-resolution satellite images from Google Earth; Sentinel-2 RGB images (Figure 2) were also used as a secondary reference when the RGB images were unavailable or contaminated by clouds or other factors.
The formation of glacial or nonglacial lakes was visually determined according to their surrounding environment, such as the distance and position to the nearest glaciers and traces of glacial movement. Glacial lakes were labeled 1 and nonglacial lakes were labeled 0 (Figure 3). The outlines of the identified lakes were drawn and stored in a vector dataset with their formation types as attributes of the lake polygon features.
3.2.3. Deep Learning-Based Alpine Lake Identification
The development of a deep learning identification model for alpine lakes is given in Figure 4. The method realized the identification of the outline and type of the alpine lake through the training of two different deep learning networks.
To meet the requirements of the deep learning segmentation algorithms, the study area was also divided into a systematic grid of tiles of 512 × 512 pixels with the projection of WGS 84/Pseudo-Mercator (EPSG: 3857) and a scale of 1: 5000 (Figure 5). The tiles in the study area (89,045) were divided into two groups along the Arun River. The western group of tiles (51,720) was used for training the deep learning network, and the eastern group (37,325) was reserved for validation (Table 2).
The visually labeled results were assigned to the grids to generate samples for training and validation. The tiles that intersected with the identified lakes were labeled as positive samples; otherwise, they were labeled as negative samples.
The sampling data for classification were collected by extracting the extent of each identified lake with a 300-m buffer to reflect the surrounding environment (Figure 6). A total of 4584 samples were collected (Table 3).
The identification of glacial lakes using deep learning in this paper consisted of two steps: segmentation and classification. The segmentation step detected the outlines of the glacial lakes by using a deep learning network trained from a Panoptic-DeepLab system [37]. The classification step distinguished the formation of the glacial and nonglacial lakes identified from the segmentation step using a model built from CoAtNet [38], which is a network that combines the strengths of Convolutional Neural Networks and Transformer Neural Networks.
The alpine lake segmentation model was trained from the Panoptic DeepLab network with the setup of the following parameters: (1) Backbone: HRNet-48; (2) Loss Function: Binary Dice Loss; (3) Batch size: 64; (4) positive and negative sample weights: 12.5:1. As the number of negative samples in the training data is much larger than the number of positive samples (12.5:1), the model would learn more about the features from negative samples, causing a data imbalance problem. Binary Dice Loss and sample weights were adopted to reduce the effects of data imbalance. The Binary Dice Loss training is more inclined to mining positive sample features to prevent them from being submerged by a large number of negative sample features. Increasing the weight of positive samples can increase the probability of positive samples being selected, so that the positive and negative samples could be equally effective in the training. The mean Intersection Over Union (MIoU) was calculated to measure the accuracy of the segmentation models:
MIoU = TP FP + FN + TP
where TP, FP, and FN are the true positives, false positives, and false negatives in the predictions of the trained network, respectively.
Four combinations of Sentinel-1, Sentinel-2, and DEM-derived variables (Table 4) were selected as model inputs to evaluate the alpine lake identification ability of different datasets.
The alpine lake classification model was trained from CoAtNet with the following parameters: (1) Model: CoAtNet-0; (2) Loss Function: BCE Loss; and (3) Batch size: 32. We selected four accuracy evaluation metrics to comprehensively validate the classification accuracy of the model: Overall Accuracy, User's Accuracy, Producer's Accuracy, and F1 Score:
Overall Accuracy = TP + TN TP + TN + FP + FN
User ′ s Accuracy = TP TP + FP
Producer ′ s Accuracy = TP TP + FP
F 1 = 2 × User ′ s Accuracy × Producer ′ s Accuracy Uses ′ s Accuracy + Producer ′ s Accuracy
3.3. Computing Environment
We set up the program running environment under a Linux system to complete the experiment. PyTorch was adopted for implementing the deep learning network framework, and the deep learning networks were trained on a computer server with 2 CPUs (Intel Xeon Gold 5118 12 Cores (Intel Corporation, Santa Clara, CA, USA)) and 16 GPUs (NVIDIA Tesla K80 12G (Nvidia Corporation, Santa Clara, CA, USA)).
4.1. Identified Alpine Lakes
A total of 4584 alpine lakes were identified, with an average area of 0.038 km2 and an average elevation of 4974 m, including 2795 glacial lakes and 1789 nonglacial lakes (Table 5). The number, area, and elevation of the glacial lakes were greater than those of the nonglacial lakes. Most of the identified alpine lakes were small: 87% of the lakes were smaller than 0.05 km2 and 42% were smaller than 0.005 km2. A total of 32 lakes were found to be larger than 1 km2, with an average area of 1.9 km2 and the largest lake of 5.5 km2. Most glacial lakes (about 57%) were located between 5000 and 5500 m above sea level.
4.2. Segmentation Accuracy
The loss of the segmentation models converged after training 20 iterations. The training loss curves were smooth, suggesting that the model achieved the imitative effect (Figure 7b). The MIoU of all samples in the four segmentation models were 96.18%, 97.21%, 97.36%, and 97.40%, respectively (Figure 7a), indicating an improved overall segmentation effect of alpine lakes as the input variables increased. The MIoU of the positive samples and the negative samples increased with the increase in input data, indicating improved completeness of alpine lakes and reduced false detection of alpine lakes as the input variables increased. In terms of the number of predicted polygons, the four segmentation models detected 1688, 1845, 1863, and 1888 lakes, respectively, of 2075 glacial lakes in the validation set. There were 3946, 1099, 928, and 841 false positive polygons in the four results, respectively. The increase in input data effectively improved the detection rate and reduced false positives.
Figure 8 presents four samples from the validation data, their visual interpretation, and corresponding predictions of the four segmentation models. The SegModel1 performed worst on alpine lake completeness and error controlling. After combining the MNDWI and Sentinel-1 data, the alpine lakes predicted by the SegModel2 and SegModel3 were more complete but included more noise caused by shadows. The use of relief data significantly reduced false detection through the results of the SegModel4. Among the four segmentation models, the model that combined the four datasets was the most accurate.
4.3. Classification Accuracy
The loss of the classification model converged after 25 iterations, and each iteration took about four minutes. The best performance of the model appeared at the 18th iteration, with Overall Accuracy of 86.41%, User's Accuracy of 86.62%, Producer's Accuracy of 85.49%, and F1-Score of 86.05%.
Out of the 949 glacial lakes, 840 were correctly classified and yielded a Producer's Accuracy of 88.5%, which was higher than that for nonglacial lakes (84.6%) (Table 6). However, 953 of the 1126 nonglacial lakes were correctly classified and yielded a User's Accuracy of 89.7%, which was higher than 82.9% for glacial lakes.
The combination of high-resolution satellite data and deep learning can accurately capture the distribution and characteristics of alpine lakes, particularly in the case of glacier lakes [31]. The presented method evaluated the effectiveness of identifying alpine lakes with multi-source data and the performance of distinguishing types of alpine lakes (glacial and nonglacial lakes). It suggested that multi-source satellite data, especially SAR and optical data, can greatly improve the detection rate and reduce false positives in alpine lake identification. The validation loss curves fluctuated downwards, but the variation decreased as more input variables were added, suggesting that the increase in information reduced the uncertainty of the segmentation (Figure 7b).
The segmentation accuracy was greatly affected by quality issues due to clouds, shadows, and other anomalies when only RGB was used as the input. These issues usually led to false positives (Figure 9a). Our experiment suggested that MNDWI could considerably improve water detection, but it is also prone to shadow effects (Figure 9b). Optical satellite data cannot penetrate clouds, leading to underestimating the number of lakes (Figure 9c); however, SAR data (VV) can overcome this limitation of optical data (Figure 9d). Additionally, SAR data are not sensitive to shallow waters. Relief data can help correcting the false positives caused by terrain shadows (Figure 9e).
The Sentinel satellite data were effective in identifying alpine lakes; however, the 10-m resolution data struggled with small moraine thaw lakes, which are hard to identify even through visual interpretation. Errors and inconsistencies in visual interpretation could be propagated into the model and affect the performance of segmentation and classification. Satellite data with higher resolutions could improve the ability to identify alpine lakes, particularly small ones. High-resolution images could also help improve the quality of interpretation by producing high-quality training data, which are key to the success of mapping alpine lakes in extreme mountainous environments.
Both deep learning models with a single RGB input (DL-RGB) and multiple inputs (DL-Multisource) produced significantly higher accuracy than the traditional approach of applying a threshold to the satellite-derived water index (MNDWI), suggesting that a deep learning method performs better for water detection (Table 7).
The MNDWI method captured most lakes in the test region, demonstrating its effectiveness in detecting water. However, the method produced many false positives, including a large number of rivers. This situation was caused by the limitation of spectral algorithm, which is not sensitive to the spatial characteristics of water bodies. The deep learning algorithm can integrate the spectral and spatial characteristics of water bodies to achieve a better identification effect. The DL-RGB method produced fewer false positives than the MNDWI, but was conservative at lake detection by capturing the least number of lakes among the three methods. The DL-Multisource method detected slightly fewer lakes than the MNDWI, with considerably fewer false positives than the other two methods, indicating that deep learning with multi-source inputs performed the best among the three methods regarding both water detection ability and misidentification.
In this paper, we compiled an inventory of alpine lakes during 2016–2020 for the Eastern Himalayas, identifying 4584 lakes, including 2795 glacial lakes. We extracted the published datasets for the region and compared them to the glacial lakes in the Third Pole Environment (TPE) (V1.0) (2010) [39] and inventory data of glacial lake in western China (2015) [40]. The two datasets reported 501 and 533 lakes, less than 20% of the lakes identified by our inventory (Table 8). Although the two datasets were produced for different years (2010 and 2015), the slow temporal dynamics of alpine lakes were unlikely to cause significant differences between the datasets. The two datasets were both produced from visual interpretation of the 30-m resolution Landsat images. Examples showed glacial lakes that clearly existed between 2009 and 2018 but were missed in both datasets (Figure 10a). The coarser resolution of Landsat data could have contributed to the omission of small lakes, especially glacial thaw lakes (Figure 10b), and the lower number of reported lakes compared to our inventory, which was produced from Sentinel data.
Our inventory captured nearly all glacial lakes identified by the two reference datasets, suggesting low commission errors with our inventory compared to the two datasets. However, only 120 glacial lakes were reported by both datasets (Figure 10c). The lack of consistency between them is likely due to differences in methods and guidelines adopted by the two datasets. The comparison also signals the urgency for mapping glacial lakes at finer scales with higher-resolution satellite data to fill the gaps in our understanding of glacial lakes, especially regarding those small in size but high in numbers.
In this paper, we presented an automatic method for the identification of glacial lakes based on deep learning and multi-source satellite data, including optical and SAR data. Compared to traditional spectral-based water detection methods, the deep learning-based methods presented considerably improved water detection ability, with significantly reduced overestimation. The inclusion of a water index (MNDWI), a SAR band (Sentinel-1 VV), and a terrain band (Relief), in addition to RGB images, as inputs for the deep learning method further improved the model performances. Although the transferability of the model has been evaluated in eastern Himalayas, the model may not perform well in regions outside of the Himalayas due to limited representation in the training data. Incorporating the training data of other regions would further improve the model's ability for identifying alpine lakes worldwide.
An alpine lake inventory consisting of 2075 glacial and 1789 nonglacial lakes was compiled for the Eastern Himalayas; this value is five times higher than the previously reported number of glacial lakes in the two previously existing datasets. The inventory unveiled a large number of glacial lakes that were missed by the existing datasets, especially small glacial thaw lakes, indicating considerable knowledge gaps. The combination of deep learning and multi-source high-solution satellite data demonstrated great potential for mapping small alpine lakes in extreme environments in the Himalayas and other part of the world, such as in Greenland and Antarctica. The results will provide critical information for understanding these ecosystems and early warning of glacial lake outburst floods.
M.F., J.X. and Y.S. designed the research concept. Y.S., K.Z. and K.S. performed the data collection for experiments. J.X. implemented the automated alpine lake identification method and analyzed the results. M.F. and J.X. drafted the manuscript. D.Y. contributed to the data visualization. All authors discussed and reviewed the paper prior to submission. All authors have read and agreed to the published version of the manuscript.
This research was funded by the National Natural Science Foundation of China (42171140).
Alpine lake inventory data set to this article can be found online at https://doi.org/10.11888/Cryos.tpdc.300056 (accessed on 4 January 2023).
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Figure 1. Study area in the Eastern Himalayas (85.2–89° E).
Figure 2. Examples of Sentinel-2 RGB composites of alpine lakes and corresponding labels used for segmentation.
Figure 3. Examples of alpine lakes and corresponding labels used for classification.
Figure 4. Flow diagram of the methods in the study.
Figure 5. Distribution of training and validation sample tiles in the segmentation dataset.
Figure 6. Distribution of training and validation samples used for classification.
Figure 7. Comparison of four segmentation models in the training and validation process: (a) MIoU; (b) loss.
Figure 8. Example results predicted by the four segmentation models (red indicates where predicted results do not match visual interpretation).
Figure 9. Advantages and problems in different scenarios: (a) False positives caused by using only true color images. (b) False positives extracted by MNDWI due to shadows. (c) A lake covered in clouds (d) was extracted after the addition of VV. (e) Shadow removal after adding Relief data.
Figure 10. Comparison of lake inventories from different years: (a) We found some glacial lakes that were not mapped in the 2010 and 2015 inventories, according to historical images from different periods obtained from Google Earth. (b) Most glacial thaw lakes were not mapped in the 2010 and 2015 inventories. (c) Lack of consistency between the 2010 and 2015 inventories.
Table 1. Input variables derived for training and driving deep learning networks, including the Sentinel-1 vertical-vertical (VV) dual polarization SAR data, Sentinel-2 red-green-blue (RGB) and MNDWI variables, and the Relief variable derived from the ALSO PALSAR DEM.
Sentinel-1 VV SAR
Sentinel-2 RGB, MNDWI Optical
ALOS PALSAR Relief DEM
Table 2. Number of total, positive, and negative tiles in the study area used for training and validation.
Positive 3938 3476 7414
Negative 47,782 33,849 81,631
Total 51,720 37,325 89,045
Table 3. Number of glacial and nonglacial lakes in the study area used for training and validation.
Glacial Lakes 1881 949 2795
Nonglacial Lakes 663 1126 1789
Total 2544 2075 4584
Table 4. Input data combinations for driving the segmentation models.
SegModel1 RGB
SegModel2 RGB, MNDWI
SegModel3 RGB, MNDWI, VV
SegModel4 RGB, MNDWI, VV, Relief
Table 5. Area, number, and elevation of the identified alpine lakes (glacial and nonglacial); Differences derived from median of all the identified and interpreted alpine lakes.
Glacial Lakes 2795 148 4 × 10−4 5.5 5.3 × 10−2 1.7 × 10−4 3690 5971 5145 11
Nonglacial Lakes 1789 42 4 × 10−4 2.1 2.4 × 10−2 −6.6 × 10−4 1496 5890 4706 27
Table 6. Confusion matrix of the alpine lake classification.
Visual Interpretation
Glacial Lakes
Nonglacial Lakes
User's Accuracy
Prediction Glacial Lakes 840 173 1013 82.9%
Nonglacial Lakes 109 953 1062 89.7%
Total 949 1126
Producer's Accuracy 88.5% 84.6%
Table 7. Comparison of three methods for identifying alpine lakes.
MNDWI
DL-RGB
DL-Multisource
Visual Interpretation Number 2075 2075 2075
Detected Lakes Number 1939 1688 1888
False Positives Number 8161 3946 841
MIoU 88.55% 96.18% 97.40%
Table 8. Three inventories of glacial lakes in different periods.
Glacial lakes in the TPE (V1.0) (2010) [39] 501
Inventory data of glacial lake in west China (2015) [40] 533
Inventory created in our study (2016–2020) 2795
Xu, J.; Feng, M.; Sui, Y.; Yan, D.; Zhang, K.; Shi, K. Identifying Alpine Lakes in the Eastern Himalayas Using Deep Learning. Water 2023, 15, 229. https://doi.org/10.3390/w15020229
Xu J, Feng M, Sui Y, Yan D, Zhang K, Shi K. Identifying Alpine Lakes in the Eastern Himalayas Using Deep Learning. Water. 2023; 15(2):229. https://doi.org/10.3390/w15020229
Xu, Jinhao, Min Feng, Yijie Sui, Dezhao Yan, Kuo Zhang, and Kaidan Shi. 2023. "Identifying Alpine Lakes in the Eastern Himalayas Using Deep Learning" Water 15, no. 2: 229. https://doi.org/10.3390/w15020229


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