Application of Artificial Intelligence in Land Use and Land Cover Mapping II
Material type:
ArticlePublication details: MDPI - Multidisciplinary Digital Publishing Institute 2025Description: 1 electronic resource (234 p.)Content type: - text
- computer
- online resource
- 9783725839919
- 9783725839926
- Reference, Information and Interdisciplinary subjects
- Research and information: general
- Billion Tree Tsunami Project (BTTP)
- CFLR model
- CNN
- Google Earth Engine
- Google Earth Engine (GEE)
- Honghe Hani Rice Terraces
- LULC transitions
- Landsat
- Master Plan 2050
- Ravi Urban Development Plan (RUDP)
- SegFormer
- accuracy assessment
- active constraint learning
- aggregated feature
- backbone network
- building extraction
- buildings and waters
- class activation map
- coastal wetland
- consistency analysis
- constrained clustering
- crop mapping
- deep learning
- double branch
- fire occurrence
- high-resolution remote sensing image
- land cover
- land use
- land use classification
- land use policies
- landcover classification
- machine learning
- phenology
- random forest
- remote sensing
- remote sensing images
- self-attentive aggregation
- semantic segmentation
- sentinel
- stratified random sampling
- synthetic aperture radar (SAR)
- time series
- time-series images
- validation dataset
- vegetation dynamics
- weakly supervised semantic segmentation
- wildfire assessment
Open Access Unrestricted online access star
Advances in Earth observation and high-performance computing are revolutionizing how we monitor land cover and support sustainable development. This volume explores cutting-edge methods in land cover classification, highlighting deep learning applications such as semantic segmentation, object detection, and temporal analysis. Key contributions include the ABNet model for enhanced feature representation, accuracy assessments of 30-meter land cover products, CNN-based wildfire mapping, and the segmentation of China's coastal wetlands. These studies showcase AI's growing role in environmental monitoring and promote innovative and interdisciplinary solutions for managing landscape changes.
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eng
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