AIMC Topic: Satellite Imagery

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Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States.

Proceedings of the National Academy of Sciences of the United States of America
The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic facto...

An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India.

Environmental monitoring and assessment
Determination of above ground biomass (AGB) of any forest is a longstanding scientific endeavor, which helps to estimate net primary productivity, carbon stock and other biophysical parameters of that forest. With advancement of geospatial technology...

A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.

PloS one
Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for es...

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.

Computational intelligence and neuroscience
A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study becau...

The sky's the limit: improving satellite imagery data literacy to address non-communicable diseases.

International health
Low-and middle-income countries experience 77% of the world's premature deaths caused by non-communicable diseases, and their underlying health determinant data are often scarce and inaccurate. Improving satellite imagery data literacy worldwide is a...

Spatiotemporal variations in Pearl River plume dispersion over the last decade based on VIIRS-derived sea surface salinity.

Marine pollution bulletin
A river plume indicates the dispersion and transport path of pollutants from runoff, monitoring the spatiotemporal variation of river plume distribution from space is crucial for marine environmental governance. This study focuses on the Pearl River ...

Review on sea water quality (SWQ) monitoring using satellite remote sensing techniques (SRST).

Marine pollution bulletin
Due to extensive anthropogenic activities in coastal areas and rivers connected to the seas, effective and timely monitoring the sea water quality (SWQ) is crucial for maintaining ecosystem health. SWQ monitoring involves examining the chemical, phys...

Brick Kiln Dataset for Pakistan's IGP Region Using AI.

Scientific data
Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution source...

Geospatial artificial intelligence for detection and mapping of small water bodies in satellite imagery.

Environmental monitoring and assessment
Remote sensing (RS) data is extensively used in the observation and management of surface water and the detection of water bodies for studying ecological and hydrological processes. Small waterbodies are often neglected because of their tiny presence...