Satellite imagery

IARPA SMART Public Dataset

The IARPA Space-Based Machine Automated Recognition Technique (SMART) program was one of the first large-scale research program to advance the state of the art for automatically detecting, characterizing, and monitoring large-scale anthropogenic activity in global scale, multi-source, heterogeneous satellite imagery. The program leveraged and advanced the latest techniques in artificial intelligence (AI), computer vision (CV), and machine learning (ML) applied to geospatial applications.

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Time Series Satellite data

Time-series satellite images provide a sequence of spatial data captured over regular intervals, enabling the analysis of changes on the Earth's surface over time. These images are particularly useful in monitoring environmental phenomena, land use changes, urban development, and agricultural patterns. By examining the same location at different time points, time-series satellite imagery allows for the detection of trends, anomalies, and transitions in land cover.

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Ecology

Trees / NoTrees Sattellite Imagery Dataset

This dataset is derived from Sentinel-2 satellite imagery.
The main goal is to employ this dataset to train and classify images into two classes: with trees, and without trees.
The structure of the dataset is 2 folders named: "tree" (images containing trees) and "no-trees" (images without presence of trees).
Each folder contains 5200 images of this type.

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IARPA SMART Public Dataset

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Time Series Satellite data

Trees / NoTrees Sattellite Imagery Dataset