AIMC Topic: Remote Sensing Technology

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Boosting cross-modal retrieval in remote sensing via a novel unified attention network.

Neural networks : the official journal of the International Neural Network Society
With the rapid advent and abundance of remote sensing data in different modalities, cross-modal retrieval tasks have gained importance in the research community. Cross-modal retrieval belongs to the research paradigm in which the query is of one moda...

Efficient plastic detection in coastal areas with selected spectral bands.

Marine pollution bulletin
Marine plastic pollution poses significant ecological, economic, and social challenges, necessitating innovative detection, management, and mitigation solutions. Spectral imaging and optical remote sensing have proven valuable tools in detecting and ...

Deep learning models map rapid plant species changes from citizen science and remote sensing data.

Proceedings of the National Academy of Sciences of the United States of America
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learni...

Multi-source machine learning and spaceborne remote sensing data accurately predict three-dimensional soil moisture in an in-service uranium disposal cell.

Journal of environmental management
One reason arid and semi-arid environments have been used to store waste is due to low groundwater recharge, presumably limiting the potential for meteoric water to mobilize and transport contaminants into groundwater. The U.S. Department of Energy O...

Mapping of temperate upland habitats using high-resolution satellite imagery and machine learning.

Environmental monitoring and assessment
Upland habitats provide vital ecological services, yet they are highly threatened by natural and anthropogenic stressors. Monitoring these vulnerable habitats is fundamental for conservation and involves determining information about their spatial lo...

Artificial Neural Network and Remote Sensing combined to predict the Aboveground Biomass in the Cerrado biome.

Anais da Academia Brasileira de Ciencias
Cerrado is the second largest biome in Brazil, and it is responsible for providing us several ecosystem services, including the functions of storing Carbon and biodiversity conservation. In this study, we developed a modeling approach to predict the ...

Rapid estimation of soil water content based on hyperspectral reflectance combined with continuous wavelet transform, feature extraction, and extreme learning machine.

PeerJ
BACKGROUND: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and e...

Digital innovations for monitoring sustainability in food systems.

Nature food
Monitoring systems that incentivize, track and verify compliance with social and environmental standards are widespread in food systems. In particular, digital monitoring approaches using remote sensing, machine learning, big data, smartphones, platf...

ST-Phys: Unsupervised Spatio-Temporal Contrastive Remote Physiological Measurement.

IEEE journal of biomedical and health informatics
Remote photoplethysmography (rPPG) is a non-contact method that employs facial videos for measuring physiological parameters. Existing rPPG methods have achieved remarkable performance. However, the success mainly profits from supervised learning ove...

Soil organic carbon estimation using remote sensing data-driven machine learning.

PeerJ
Soil organic carbon (SOC) is a crucial component of the global carbon cycle, playing a significant role in ecosystem health and carbon balance. In this study, we focused on assessing the surface SOC content in Shandong Province based on land use type...