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Remote Sensing Applications for the Urban Environment

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.rse.2011.02.030openalex-snapshot
Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends
inferredcites →https://doi.org/10.1201/b15917openalex-snapshot
Urban Remote Sensing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4631795openalex-extracted
Examination of Air Pollutant Fluctuations During the Covid-19 Pandemic in the Urban Area of Shiraz Utilizing Sentinel-5 Satellite Data
inferred← citeshttps://doi.org/10.1002/9780470979563.ch11openalex-snapshot
Exploring the Temporal Lag Between the Structure and Function of Urban Areas
inferred← citeshttps://doi.org/10.1002/essoar.10501308.1openalex-snapshot
Sediment transport and morphodynamic modelling for coasts and shallow environments
inferred← citeshttps://doi.org/10.1002/joc.7396openalex-snapshot
Seasonal simulations of summer aerosol optical depth over the Arabian Peninsula using WRF‐Chem : Validation, climatology, and variability
inferred← citeshttps://doi.org/10.1007/s10708-020-10302-4openalex-snapshot
Land use land cover mapping and monitoring urban growth using remote sensing and GIS techniques in Mangaluru, India
inferred← citeshttps://doi.org/10.1007/s41748-019-00108-xopenalex-snapshot
Analysis of AOD from MODIS-Merged DT–DB Products Over the Arabian Peninsula
inferred← citeshttps://doi.org/10.1007/s41976-026-00307-xopenalex-snapshot
Biophysical Segmentation Neural Model: An Automatic Algorithm for Urban Environment Classification. Case of Study for the Metropolitan Zone of the Valley of Mexico
inferred← citeshttps://doi.org/10.1016/b978-0-323-89861-4.00023-3openalex-snapshot
Spatiotemporal urban sprawl and land resource assessment using Google Earth Engine platform in Lahore district, Pakistan
inferred← citeshttps://doi.org/10.1016/j.atmosenv.2018.06.011openalex-snapshot
Aerosol Optical Depth variability over the Arabian Peninsula as inferred from satellite measurements
inferred← citeshttps://doi.org/10.1016/j.envpol.2017.07.104openalex-snapshot
Assessment of AOD variability over Saudi Arabia using MODIS Deep Blue products
inferred← citeshttps://doi.org/10.1016/j.jastp.2022.105932openalex-snapshot
Dust storm ‘hot spots’ and Transport Pathways Affecting the Arabian Peninsula
inferred← citeshttps://doi.org/10.1016/j.jglr.2018.07.015openalex-snapshot
Assessment of trophic state and water quality of coastal-inland lakes based on Fuzzy Inference System
inferred← citeshttps://doi.org/10.1088/1755-1315/842/1/012070openalex-snapshot
Spatial and temporal changes of urban forest in Jeli, Kelantan
inferred← citeshttps://doi.org/10.1109/igarss.2016.7729457openalex-snapshot
Development of 2016 national imperviousness product
inferred← citeshttps://doi.org/10.1109/igarss.2018.8518013openalex-snapshot
Analysis of Different Sensor Performances in Impervious Surface Mapping
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1903026/v1openalex-snapshot
Aerosol Optical Depth over the Middle East and North Africa region in CMIP6 Models: Climatology, Variability, and Trends
inferred← citeshttps://doi.org/10.3233/jifs-179669openalex-snapshot
An improved architecture for urban building extraction based on depthwise separable convolution
inferred← citeshttps://doi.org/10.3389/fclim.2024.1384202openalex-snapshot
Climatology, trends, and future projections of aerosol optical depth over the Middle East and North Africa region in CMIP6 models
inferred← citeshttps://doi.org/10.3390/rs8100787openalex-snapshot
Synergistic Use of LiDAR and APEX Hyperspectral Data for High-Resolution Urban Land Cover Mapping
inferred← citeshttps://doi.org/10.4000/mappemonde.442openalex-snapshot
Cartographie des températures à Tunis par modélisation statistique et télédétection
inferred← citeshttps://doi.org/10.5194/isprs-archives-xlii-2-w13-1887-2019openalex-snapshot
COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER
inferred← citeshttps://doi.org/10.5194/isprs-archives-xliii-b3-2021-117-2021openalex-snapshot
THE USE OF SPECTRAL AND TEXTURAL FEATURES IN CROP TYPE MAPPING USING SENTINEL-2A IMAGES: A CASE STUDY, ÇUKUROVA REGION, TURKEY
inferred← citeshttps://doi.org/10.5593/sgem2022/2.1/s10.36openalex-snapshot
DETERMINATION OF LAND SURFACE TEMPERATURE USING SENTINEL � 3 SLTR DATA AND GEOGRAPHIC INFORMATION SYSTEM TECHNIQUES
inferred← citeshttps://doi.org/10.5772/intechopen.82729openalex-snapshot
High-Resolution Satellite Imagery Classification for Urban Form Detection
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