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Mining High Resolution Earth Observation Data Cubes

Published: 23 August 2021 Publication History

Abstract

Earth observation data is collected by ever-expanding fleets of satellites including Landsat1-8, Sentinel1 & Sentinel2, SPOT1-7 and WorldView1-3. These satellites generate at spatial resolutions (pixel size) from 30m to 31cm and provide revisit rates of as frequent as every 5 days. This allows us not only to look at high-resolution images of every corner of the Earth, but also to track events and observe change over time. During the past 5 years, medium spatial resolution satellite data (30 − 10m pixels) have developed very high temporal revisit frequencies of 5-16 days and spatial-temporal structures have been developed to manage these vast data sets. However, high resolution satellite images and rapidly increasing revisit rates create major data management and mining challenges. This work discusses six challenges of integrating observations at different times, from different sensors, at different spatial resolutions and different temporal frequencies into a unified Earth Observation Data Cube, that is, a tensor of location, time, and spectral bands. Challenges include creating a unified data cube from heterogeneous sensors, scaling geo-registration (mapping pixel between images), accounting for uncertainty across observations, imputing missing observations, broad area event detection, and ultimately, predicting the future state of our planet. With such a unified Earth Observation Data Cube in place, we describe potential application areas such as detecting anthropogenic land cover change, early warning of natural hazards, tracing movement of animals, finding missing airplanes, and rapid detection of forest fires.

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  • (2023)Soil Moisture Retrieval During Crop Growth Cycle Using Satellite SAR Time SeriesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.328018116(9302-9319)Online publication date: 2023
  • (2023)Semi-Supervised Satellite Image Segmentation Using Spatial and Temporally Informed Poisson LearningIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS52108.2023.10282967(5642-5645)Online publication date: 16-Jul-2023

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SSTD '21: Proceedings of the 17th International Symposium on Spatial and Temporal Databases
August 2021
173 pages
ISBN:9781450384254
DOI:10.1145/3469830
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Published: 23 August 2021

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  • (2023)Soil Moisture Retrieval During Crop Growth Cycle Using Satellite SAR Time SeriesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.328018116(9302-9319)Online publication date: 2023
  • (2023)Semi-Supervised Satellite Image Segmentation Using Spatial and Temporally Informed Poisson LearningIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS52108.2023.10282967(5642-5645)Online publication date: 16-Jul-2023

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