Remote-sensing imagery is widely used for agricultural monitoring, disaster management, urban planning, forest monitoring, water-resource assessment, infrastructure mapping, and environmental analysis. However, most existing remote-sensing AI solutions are developed as isolated applications for a single predefined task, such as land-cover classification, object detection, visual question answering, or change detection. These systems often require users to understand satellite-data characteristic
or change-based visual question answering from a bi-temporal image pair shall be mandatory.
Remote-sensing imagery is widely used for agricultural monitoring, disaster management, urban planning, forest monitoring, water-resource assessment, infrastructure mapping, and environmental analysis However, most existing remote-sensing AI solutions are developed as isolated applications for a single predefined task, such as land-cover classification, object detection, visual question... These systems often require users to understand satellite-data characteristic
or change-based visual question answering from a bi-temporal image pair shall be mandatory. A spatial change map may also be generated where reference masks are available. • Cross-modal pair analysis: The system must extract complementary information from a co-registered optical/multispectral and SAR image pair. • Agentic orchestration: The system must automatically select, sequence, and execute the appropriate specialist models or tools according to the query and input configuration. Representative Queries • 'Describe the land-cover and major objects visible in this image.' • 'Highlight the water body referred to in the query.' • 'What changed between these two dates, and where did the change occur?' • 'Use the optical and SAR images together to detect built-up and water-covered regions