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Appen
Remotasks
Scale AI
Geospatial Tiled Imagery
Image
Text
Classification
Data Collection
Evaluation/Rating
Geocoding
MappingProficient in labeling diverse satellite image types (e.g., multispectral, hyperspectral, SAR) for various applications (e.g., land cover classification, object detection, change detection). Adept at using specialized annotation tools and platforms tailored for satellite imagery analysis. Possess a strong understanding of geospatial concepts and coordinate systems (e.g., WGS84, UTM). LLM Training Powerhouse: Deep understanding of how labeled data shapes LLM performance, ensuring efficient and targeted annotation efforts. Skilled in generating diverse and high-quality training prompts aligned with specific LLM objectives. Capable of evaluating LLM output and identifying areas for improvement based on labeling accuracy
Scale AIText
Question AnsweringEvaluation/RatingImagery Source: Specify the origin of the satellite images (e.g., Sentinel-2, Landsat, custom provider). Image Coverage: Project covered the geographical area Los, Angelis. Number of Images: 1200 images included in the project. Resolution: Resolution was grids of 30 x 30 m spatial resolution, in 8 spectral bands).
Masters, geology
Geologist
