AIMC Topic: Remote Sensing Technology

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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
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...

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

PeerJ
Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, ., can retard their growth. With a...

An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning.

Environmental monitoring and assessment
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...

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning.

Acta tropica
Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collaps...

Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus.

Acta tropica
Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts i...

Land-use classification based on high-resolution remote sensing imagery and deep learning models.

PloS one
High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the ...

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics.

BMC medical imaging
Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based ...

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

PloS one
Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine...

Spatiotemporal variation reconstruction of total phosphorus in the Great Lakes since 2002 using remote sensing and deep neural network.

Water research
Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements a...

Ocean carbon emission prediction and management measures based on artificial intelligence remote sensing estimation in the context of carbon neutrality.

Environmental research
With rapid economic development, the gradual deterioration of the natural environment has posed unprecedented challenges to human social civilization. The marine economy, as an important part of economic development, is the breakthrough of economic t...