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Semantic Segmentation of Remotely Sensed Images for Land-use and Land-cover Classification: A Comprehensive Review (Record no. 132489)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02049nam a2200145 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250821b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Aarabhi Putty |
| 245 ## - TITLE STATEMENT | |
| Title | Semantic Segmentation of Remotely Sensed Images for Land-use and Land-cover Classification: A Comprehensive Review |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | Pages: 222-237 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. biblio.abstract | Remotely 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.<br/><br/> |
| 654 ## - SUBJECT ADDED ENTRY--FACETED TOPICAL TERMS | |
| Subject | <a href="Deep learning">Deep learning</a> |
| -- | <a href="GIS framework">GIS framework</a> |
| -- | <a href="Land-use and land-cover">Land-use and land-cover</a> |
| -- | <a href="Machine learning">Machine learning</a> |
| -- | <a href="Remote sensing">Remote sensing</a> |
| -- | <a href="Semantic segmentation">Semantic segmentation</a> |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sankar Pariserum Perumal |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 80270 |
| Host Itemnumber | 113442 |
| Place, publisher, and date of publication | New Delhi IETE |
| Title | IETE Technical Review |
| International Standard Serial Number | 0256-4602 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Location (home branch) | Sublocation or collection (holding branch) | Date acquired | Koha issues (times borrowed) | Piece designation (barcode) | Koha date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | SNDT Juhu | SNDT Juhu | 21/08/2025 | JP864.4 | 21/08/2025 | 21/08/2025 | Journal Article |