Benchmark data for: Machine Learning for geospatial vector data classification
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Benchmark data for paper 'Deep Learning for Classification Tasks on Geospatial Vector Polygons'. Core of the data is in the six numpy zip files. Each numpy zip contains the original WKT geometries as zlib compressed blobs, variable and fixed length geometry vectors, fourier descriptors, and a class dictionary.  The zlib compressed wkt strings can be decompressed with import numpy as npimport zlibloaded = np.load('archaeology_train_v8.npz')wkts_zipped = loaded['wkts_zlib_compressed']for wkt_zipped in wkts_zipped:&nbsp wkt = str.decode(zlib.decompress(wkt_zipped))</code' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Deep learning","Geometry","Machine learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Benchmark data for paper 'Deep Learning for Classification Tasks on Geospatial Vector Polygons'. Core of the data is in the six numpy zip files. Each numpy zip contains the original WKT geometries as zlib compressed blobs, variable and fixed length geometry vectors, fourier descriptors, and a class dictionary.  The zlib compressed wkt strings can be decompressed with import numpy as npimport zlibloaded = np.load('archaeology_train_v8.npz')wkts_zipped = loaded['wkts_zlib_compressed']for wkt_zipped in wkts_zipped:&nbsp wkt = str.decode(zlib.decompress(wkt_zipped))</code' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Benchmark data for paper 'Deep Learning for Classification Tasks on Geospatial Vector Polygons'. Core of the data is in the six numpy zip files. Each numpy zip contains the original WKT geometries as zlib compressed blobs, variable and fixed length geometry vectors, fourier descriptors, and a class dictionary.  The zlib compressed wkt strings can be decompressed with import numpy as npimport zlibloaded = np.load('archaeology_train_v8.npz')wkts_zipped = loaded['wkts_zlib_compressed']for wkt_zipped in wkts_zipped:&nbsp wkt = str.decode(zlib.decompress(wkt_zipped))</code
Deep learning; Geometry; Machine learning