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Python-Based Open-Source Tool for Automating Seleno-Referencing of Chandrayaan-2 Hyper-Spectral Data Cubes
ISRO’s Imaging InfraRed Spectrometer (IIRS) onboard Chandrayaan-2 is relatively the most advanced spectrometer in the lunar orbit till date. IIRS...
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AI-driven reinforced optimal cloud resource allocation (ROCRA) for high-speed satellite imagery data processing
The emergence of cloud computing has brought attention to the broad issue of resource allocation across domains. A thorough and adaptable solution to...
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Land use land cover classification using Sentinel imagery based on deep learning models
For proper planning of urban infrastructures such as road networks, pipelines, and other linear engineering structures, it is necessary to construct...
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Superpixel for seagrass map**: a novel method using PlanetScope imagery and machine learning in Tauranga harbour, New Zealand
Seagrass ecosystem provides valuable ecosystem services and is significant blue carbon sink. This resource, however, has been degraded across the...
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The Utility of Gordon’s Standard NIR Empirical Atmospheric Correction Algorithm for Unmanned Aerial Vehicle Imagery
Unmanned aerial vehicle (UAV) imaging has been increasingly applied for environmental monitoring due to its high spatial resolution. However, the...
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One versus all: identifiability with a multi-hazard and multiclass building damage imagery dataset and a deep learning neural network
This paper analyzed the quality of the xBD image-training dataset for identifying building damage across a variety of natural hazards using deep...
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Use of multispectral and thermal satellite imagery to determine crop water requirements using SEBAL, METRIC, and SWAP models in hot and hyper-arid Oman
Hyper-arid regions, such as the Sultanate of Oman, require precise information regarding actual water application to develop efficient irrigation...
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A Spatial-Aware Neural Network for Inversion of Shallow Water Depth from WorldView-02 High-Spectral-Resolution Imagery
Remote sensing shallow water depth inversion is to automatically predict the water depth value of 0–30 m based on the extraction of the significative...
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Use of Multispectral and Hyperspectral Satellite Imagery for Monitoring Waterbodies and Wetlands
Timely and accurate change detection (CD) of Earth’s surface features is important for understanding interactions between human and natural...
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Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks
After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage...
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Glacial lakes map** using satellite images and deep learning algorithms in Northwestern Indian Himalayas
Global climate change's influence on the Himalayan glaciers has initiated glacier retreats, resulting in the development and evolution of glacial...
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Identification of Spectral Bands to Discriminate Wheat Spot Blotch using in Situ Hyperspectral Data
The present study is focused on electromagnetic spectra generated in the hyperspectral domain to detect and discriminate healthy wheat crops from...
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Evaluating the addition of radar with optical data for vegetation map** in a montane region in Sri Lanka
The use of freely-available multi-source imagery for map** vegetation in montane terrain is important for many develo** countries that do not...
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Detection and Classification of Floating and Submerged Polluted Targets at Turbid Water Using Remote Sensing Hyperspectral Imaging
Hyperspectral imaging (HI) is gaining prominence for its rapid and precise remote sensing in aquatic environments to identify and categorize a number...
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Extracting Soil Moisture from Fengyun-3D Medium Resolution Spectral Imager-II Imagery by Using a Deep Belief Network
Obtaining continuous and high-quality soil moisture (SM) data is important in scientific research and applications, especially for agriculture,...
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Simulating crop yield estimation and prediction through geospatial data for specific regional analysis
Geospatial imagery play a key role in deciding land usage for agrarian planning and assessment by acknowledging the food security problems, impacts...
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Machine learning based object-level crop classification of PlanetScope data at South India Basin
Crop classification is of great significance as it opens doors to agricultural inventory and research related to crop map**, crop yield, and...
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Hyperspectral and LiDAR data fusion in features based classification
Modern technology fusions are referred to as a significant source in remote sensing. It is one of the various sources that incorporate the use of...
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Classification and detection of natural disasters using machine learning and deep learning techniques: A review
For efficient disaster management, it is essential to identify and categorize natural disasters. The classical approaches and current technological...
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Machine learning based urban land cover classification using PolInSAR data: a study with ALOS-2 and RADARSAT-2 datasets
A substantial variation in the land cover dynamics has been observed as a consequence of increasing urban expansion. Polarimetric synthetic aperture...
