AIMC Topic: Satellite Imagery
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Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery.
Environmental monitoring and assessment
Jul 16, 2024
Accurate retrieval of LST is crucial for understanding and mitigating the effects of urban heat islands, and ultimately addressing the broader challenge of global warming. This study emphasizes the importance of a single day satellite imageries for l...
Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HEAT Center study protocol.
BMJ open
Jun 18, 2024
INTRODUCTION: African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the compl...
Novel artificial intelligence assisted Landsat-8 imagery analysis for mango orchard detection and area mapping.
PloS one
Jun 14, 2024
The mango fruit plays a crucial role in providing essential nutrients to the human body and Pakistani mangoes are highly coveted worldwide. The escalating demand for agricultural products necessitates enhanced methods for monitoring and managing agri...
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...
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