AIMC Topic: Satellite Imagery
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Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models.
Environmental monitoring and assessment
May 22, 2024
In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especiall...
An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning.
Environmental monitoring and assessment
May 6, 2024
Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the con...
Unmasking the sky: high-resolution PM prediction in Texas using machine learning techniques.
Journal of exposure science & environmental epidemiology
Apr 1, 2024
BACKGROUND: Although PM (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.
Evaluating spatially enabled machine learning approaches to depth to bedrock mapping, Alberta, Canada.
PloS one
Mar 27, 2024
Maps showing the thickness of sediments above the bedrock (depth to bedrock, or DTB) are important for many geoscience studies and are necessary for many hydrogeological, engineering, mining, and forestry applications. However, it can be difficult to...
Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning.
Journal of environmental management
Mar 12, 2024
Robust quantification of vegetative biomass using satellite imagery using one or more forms of machine learning (ML) has hitherto been hindered by the extent and quality of training data. Here, we showcase how ML predictive demonstrably improves when...
ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012-2023 with Landsat series data.
Environmental monitoring and assessment
Feb 5, 2024
Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from differe...
A Vision Transformer Model for Convolution-Free Multilabel Classification of Satellite Imagery in Deforestation Monitoring.
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Understanding the dynamics of deforestation and land uses of neighboring areas is of vital importance for the design and development of appropriate forest conservation and management policies. In this article, we approach deforestation as a multilabe...
State-of-the-Art Deep Learning Methods for Objects Detection in Remote Sensing Satellite Images.
Sensors (Basel, Switzerland)
Jun 23, 2023
Object detection in remotely sensed satellite images is critical to socio-economic, bio-physical, and environmental monitoring, necessary for the prevention of natural disasters such as flooding and fires, socio-economic service delivery, and genera...
Higher depression risks in medium- than in high-density urban form across Denmark.
Science advances
May 24, 2023
Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensio...
A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images.
Sensors (Basel, Switzerland)
Nov 28, 2022
Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth's surface for more than 4 years and has become an important data source for a large ...
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