Geospatial Data Annotator — Shrimp Aquaculture

Longline Environment Ltd is a UK-registered environmental consultancy specialising in aquaculture risk, remote sensing, and spatial planning. The company is recruiting an experienced annotator to build a high-resolution training dataset for machine learning models designed to detect and characterise shrimp pond infrastructure from aerial and satellite imagery. Responsibilities include annotating high-resolution drone and satellite imagery of shrimp pond farms; labeling pond polygons, embankment edges, inlet/outlet canals, aerators, paddlewheels and associated farm infrastructure; applying consistent annotation schemas across intensive, semi-intensive, traditional/extensive and silvofishery ponds; validating and quality-checking annotations; maintaining annotation logs and flagging ambiguous or low-quality imagery; working in QGIS or equivalent GIS environments to manage spatial annotation layers; coordinating with the GIS & ML Lead on label taxonomy and edge cases; and delivering annotated datasets in agreed formats including GeoJSON, COCO and YOLO. Requirements include at least 2 years of hands-on experience working in or directly with shrimp pond operations; practical familiarity with shrimp pond farm layouts, pond geometries, embankments, water management infrastructure and aeration systems; ability to distinguish between species-specific pond types such as Litopenaeus vannamei and Penaeus monodon; understanding of pond lifecycle stages and seasonal/production-cycle appearance changes; proficiency in QGIS for polygon digitising, layer management and spatial data export; comfort working with high-resolution raster imagery and georeferenced files; familiarity with GeoJSON and/or shapefile formats; basic understanding of coordinate reference systems and image scale; strong attention to detail; written English proficiency; reliable internet; and ability to report progress weekly. Desirable skills include experience with Label Studio, CVAT or Roboflow; familiarity with COCO JSON and YOLO object detection labels; previous drone or satellite imagery annotation experience; knowledge of Southeast Asia or Middle East shrimp aquaculture systems; and Python or scripting knowledge for annotation format conversion. Benefits include a competitive freelance day/task rate, fully remote flexible working arrangement, involvement in a technically ambitious ML project with real-world application in global aquaculture, and potential extension into longer-term geospatial or AquaScape data work. Application instructions: send a short CV or profile, 2–3 sentences describing shrimp aquaculture experience, examples of GIS or annotation work if available, daily/weekly availability and rate expectations to [email protected] with the subject line "Shrimp Annotator Application".