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100			_aAarabhi Putty
245			_aSemantic Segmentation of Remotely Sensed Images for Land-use and Land-cover Classification: A Comprehensive Review
300			_aPages: 222-237
520			_aRemotely Sensed Images (RSI) based land-use and land-cover (LULC) mapping facilitates applications such as forest logging, biodiversity protection, and urban topographical kinetics. This process has gained more attention with the widespread availability of geospatial and remote sensing data. With recent advances in machine learning and the possibility of processing nearly real-time information on the computer, LULC mapping methods broadly fall into two categories: (i) framework-dependent algorithms, where mappings are done using the in-built algorithms in Geographical Information System (GIS) software and (ii) framework-independent algorithms, which are mainly based on deep learning techniques. Both approaches have their unique advantages and challenges. Along with the working patterns and performances of these two methodologies, this comprehensive review thoroughly analyzes deep learning architectures catering different technical capabilities like feature extraction, boundary extraction, transformer-based mechanism based mechanism, attention mechanism, pyramid pooling and lightweight models. To fine-tune these semantic segmentation processes, current technical and domain challenges and insights into future directions for analysing RSIs of varying spatial and temporal resolutions are summarized. Cross domain users with application specific requirements can make use of this study to select appropriate LULC semantic segmentation models.
654			_aDeep learning _aGIS framework _aLand-use and land-cover _aMachine learning _aRemote sensing _aSemantic segmentation
700			_aSankar Pariserum Perumal
773	0		_080270 _9113442 _dNew Delhi IETE _tIETE Technical Review _x0256-4602
942			_cJA
999			_c132489 _d132489