Speaker notes
Visual data connect sensing, computational analysis and spatial decisions. Data entering a technical system. Sensors produce images, coordinates and timestamps. Computer vision maps images to categories, object locations or pixel labels. GIS connects these outputs to buildings and roads. Each operation needs an explicit input format, output structure and source record. Analysis, generation and tool use. Analytical models estimate properties of observed scenes. Generative models propose images or designs under specified conditions. Agents can retrieve information, call models and write files. These capabilities still depend on correct coordinates, suitable scales and adequate coverage. The technical foundation. This lesson connects image_id, building_id and sample_id through one case. Image arrays, camera projection, road sampling, API responses and file verification establish what subsequent models receive, which objects they describe and where their outputs belong. A building survey. Input: a documented building photograph and road geometry. Intermediate outputs: an RGB array, candidate observation points and viewing parameters. Deliverable: a spatial dataset that can be reopened and checked. Material classification remains a separate modeling task.
References: docs.opencv.org · commons.wikimedia.org
Photographs: A.Schneider83 / CC BY-SA 4.0 · Martin Kraft / CC BY-SA 3.0 · Jelson25 / CC BY-SA 3.0. Derived images retain their source licenses. OSM contributors / ODbL.