Land-use and land-cover change analysis compares landscape composition across time. GeoRetina AI can summarize multi-year trends, identify the classes gaining or losing area, and map where change concentrates.
What It Answers
- How much built area, tree cover, crop cover, or bare ground changed?
- Which land-cover classes are increasing or decreasing?
- Where are the most important change hotspots?
- Which periods show the strongest shifts?
Prompt Examples
Multi-year trend
Show land-cover trends for @city_boundary from 2018 to 2025.
Before and after
Compare land cover in @site_area between summer 2020 and summer 2025.
Built area
Quantify built-area expansion in @growth_corridor from 2018 to 2025.
Natural cover
Identify where natural or non-built cover declined in @watershed.
Reading the Output
GeoRetina AI may return a map, a time slider or timeline, charts, and a summary of dominant changes. Use the chart to understand magnitude and the map to understand location.
Best Practices
- Use consistent seasons for fair comparisons.
- Ask for the exact class you care about when the decision depends on one class.
- Use Super mode if the result needs a narrative report or presentation-ready figures.
- Follow up with vector queries when you need to connect change areas to parcels, buildings, roads, or other features.
Useful Follow-ups
- "Which polygons have the largest built-area gains?"
- "Summarize the top three land-cover transitions."
- "Create a report for a planning audience."
- "Export the changed-area layer."