mlsanityData to 7 Oct 2026

AiTLAS: Artificial Intelligence Toolbox for Earth Observation

arXiv 2201.08789 ↗First posted 21 Jan 2022cs.CVcited by 7

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Abstract (arXiv)

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which further allows benchmarking of various existing and novel AI methods tailored for EO data.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 26 of its 97 references that are in the map. First posted on arXiv 21 Jan 2022.

Builds on

26 of 97 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 2.

7 papers.

  1. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  2. 2022Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classificationcited by 18
  3. 2023In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classificationcited by 4
  4. 2024Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormercited by 2
  5. 2024Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learningcited by 0
  6. 2025Geospatial Machine Learning Librariescited by 0
  7. 2025Few-Shot Remote Sensing Image Scene Classification with CLIP and Prompt Learningcited by 0