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Application of Artificial Intelligence in Land Use and Land Cover Mapping II

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 detect...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2025
Matèries:
deep learning
remote sensing
land use classification
sentinel
time series
synthetic aperture radar (SAR)
crop mapping
time-series images
constrained clustering
active constraint learning
Google Earth Engine (GEE)
LULC transitions
vegetation dynamics
CFLR model
land use policies
Billion Tree Tsunami Project (BTTP)
Ravi Urban Development Plan (RUDP)
Master Plan 2050
building extraction
high-resolution remote sensing image
weakly supervised semantic segmentation
self-attentive aggregation
class activation map
double branch
CNN
semantic segmentation
buildings and waters
Honghe Hani Rice Terraces
Landsat
land use/land cover
phenology
Google Earth Engine
SegFormer
coastal wetland
remote sensing images
machine learning
wildfire assessment
random forest
fire occurrence
land cover
validation dataset
accuracy assessment
consistency analysis
stratified random sampling
backbone network
landcover classification
aggregated feature
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
Accés en línia:ONIX_20250812T110751_9783725839926_280
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Sumari: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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