Many Vermont towns lack up-to-date information about the location of built structures within their borders.  This information gap can hinder timely emergency response. With increased frequency of extreme weather events in the state, accurate addresses and housing data are critical for getting help to where it is needed.  Lack of detailed and current land use mapping also impedes a town’s ability to plan for housing development, conservation and infrastructure upgrades.   

Available data might represent one point in time and does not accurately capture changes to landcover and land use. Maintaining current data and mapping is a challenge for municipalities, which lack resources to update these data regularly.

Currently, the state uses satellite information (LandSat) to track development changes, which often requires manual input into E911.

The Vermont Construction and Landscape Mapping Initiative (V-CALM) will use high-resolution satellite imagery to detect land use changes in real-time and make this information available to state and local agencies.  These updates will provide a more detailed understanding of evolving patterns in land use.  As a result, towns will have better data to help address housing availability, inform municipal planning capacity, and better understand rural population dynamics and exposure to natural hazards.   

UVM’s Spatial Analysis Lab will bring its expertise in machine learning and artificial intelligence models to capture landcover and land use across the state and to keep this information up to date. V-CALM data will be available for use by agencies like the State of Vermont Enhanced 911 Board, the Vermont Housing Finance Agency, and the Department of Housing and Community Development. Significantly, new development will be verified and added to the E911 database. 

UVM’s Spatial Analysis Laboratory (SAL) will work with the Vermont Center for Geographic Information (VCGI) to develop VCALM.  The partnership will work to correct missing addresses to Vermont’s E911 system and to add those to the publicly available dataset monthly. SAL and VCGI will automate updates to the dataset using machine learning and artificial intelligence models. Overall, the partners hope to improve data accuracy and reduce reporting burdens for rural communities. It seeks to provide up-to-date and tangible data to towns so they can make data-driven decisions and apply for state and federal funding and assistance with greater ease.