AiTLAS: Artificial Intelligence Toolbox for Earth Observation
원문 초록
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.
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Scaling-Up the Pretraining of the Earth Observation Foundation Model PhilEO to the MajorTOM Dataset
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition
AI-Driven MRI-based Brain Tumour Segmentation Benchmarking
MetaFormer and CNN Hybrid Model for Polyp Image Segmentation
FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
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