The changing climate and increasingly unpredictable sea ice conditions have created life-threatening risks for Inuit, the residents of the Arctic, who depend on the ice for transportation and livelihood. In response, they are turning to technology (e.g., RADAR imagery from the Canadian RADARSAT satellite) to augment their traditional knowledge of the ice and to map potential hazards. The difficulty lies in the actual RADAR interpretation process. In order to support understanding of the RADAR image content, we introduce a work-in-progress (WIP), INTUIT, a physicalization that represents the RADAR reflection strength, which is highly influenced by surface roughness, as a tactile texture. Such tactile texture is made by resampling the RADAR imagery to a number of UV cells and mapping the average brightness value of each cell to a physical variable. A proof of concept was designed for a region in Baffin Island (Nunavut) and sent to the Arctic for initial feedback. Preliminary study results are promising: it is expected that INTUIT will facilitate the interpretation learning process for RADAR imagery.
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http://dx.doi.org/10.1109/MCG.2023.3286228 | DOI Listing |
Sensors (Basel)
January 2025
Departamento de Geografía, Facultad de Ciencias, Universidad de la República, Montevideo 4225, Uruguay.
Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers.
View Article and Find Full Text PDFPlants (Basel)
December 2024
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Accurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize density estimation by integrating optical and radar data, addressing these challenges with a unique mapping strategy. The strategy combines available data selection, key feature extraction, and optimization to improve accuracy across diverse growth stages.
View Article and Find Full Text PDFSensors (Basel)
December 2024
ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.
Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India.
In short-range microwave imaging, the collection of data in real environments for the purpose of developing techniques for target detection is very cumbersome. Simultaneously, to develop effective and efficient AI/ML-based techniques for target detection, a sufficiently large dataset is required. Therefore, to complement labor-intensive and tedious experimental data collected in a real cluttered environment, synthetic data generation via cost-efficient electromagnetic wave propagation simulations is explored in this article.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, 2000, South Africa.
The grassland ecosystem forms a critical part of the natural ecosystem, covering up to 15-26% of the Earth's land surface. Grassland significantly impacts the carbon cycle and climate regulation by storing carbon dioxide. The organic matter found in grassland biomass, which acts as a carbon source, greatly expands the carbon stock in terrestrial ecosystems.
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