Datasets for deep learning with satellite & aerial imagery
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Updated
May 30, 2024
Datasets for deep learning with satellite & aerial imagery
Techniques for deep learning with satellite & aerial imagery
A comprehensive list of NASA Earth science data products
Satellite SST maps and buoy temperature data for monitoring Marine Heatwaves
Software behind the RACE dashboard by ESA and the European Commission (https://race.esa.int), the Green Transition Information Factory - GTIF (https://gtif.esa.int), as well as the Earth Observing Dashboard by NASA, ESA, and JAXA (https://eodashboard.org)
🐍 The official Python client library for <geosys/> APIs.
The new version of the INAMHI GEOGLOWS portal, developed using Angular, Django, and Docker for enhanced scalability and performance.
AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.
Using web scraped flight path data to visualize orbiting of satellites around the earth
DINCAE (Data-Interpolating Convolutional Auto-Encoder) is a neural network to reconstruct missing data in satellite observations.
DSen2-CR: A network for removing clouds from Sentinel-2 images. This repo contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset.
🛰️ Process raster data in python
framework for large-scale SAR satellite data processing
A pure Julia package for querying and downloading Landsat data.
acquire satelital decode data C/N margin dB level
This project leverages satellite data and machine learning to predict rice crop locations in Vietnam's An Giang province, showcasing the fusion of remote sensing and data science.
This library aims to simplify any process working with different sets of EO data handled by EOReader
SlideRule Earth Example Notebooks: On-demand, cloud-based processing of satellite mission data (NASA ICESat-2, GEDI, ArcticDEM/REMA, HLS)
Intake catalog referencing ISCCP HGH, HGM, HGG products
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