Innovate enhanced the geospatial products by incorporating technologies such as AI feature extraction and 3D modeling. We used geoprocessing methodologies and web-based applications to create planning decision support frameworks from a neighborhood level to the national scale. We employed server-side geoprocessing tools to further generate analytical derivatives that we then used within cartographic products, dynamic web-based and mobile applications as well as supported graphics for technical reports. Innovate used Esri’s ArcHydro software to process overland stormwater flow data from DEMs and DSMs to model how water travels, accumulates, and gains velocity over topography. The by-product of this analysis provides a municipality with the ability to identify how changing its infrastructure from impervious to more green spaces can impact the potential for flooding.
Sr. IT/GIS Strategist & Architect | Project Manager | Subject Matter Expert | Business DevelopmentDynamic Senior IT/GIS Strategist & Architect with over 30 years managing and supporting IT and geospatial application development projects to implement complex and advanced geospatial... Read More →
Everyone is talking or attempting to use Artificial Intelligence, Machine Learning, or AI. Industry leaders would say that data is the most important ingredient to 'make' AI work. A common theme for the proper or efficient use of AI is a need for 'good data' or data of the highest quality. GIS Practitioners have coined the phrase 'Garbage In and Garbage Out' as a rallying cry for better data since the formation of the discipline. Most organizations have some set or repository of data they utilize for their business, and some even create their data. The state of quality of these data repositories can fluctuate from abysmal to pristine. So how can organizations improve the quality of existing data or ensure that the data being collected now, is the best the organization can make it? This presentation claims that data must be valued as highly as revenue or profits if data quality is to improve. If data are valued highly, then care is taken as it is collected, compiled, and collated, and arguably, quality will improve, and there is a fighting chance for the use of AI to succeed for whatever purposes is required.
The UVM Spatial Analysis Lab has a long history of high quality and high impact GIS work. In the last year alone the SAL has performed emergency response to multiple major flood events, taught numerous workshops for first responders, state/federal employees, and highschol students and produced tens of thousands of square miles of high resolution land cover data, mapping areas that contain more than 9% of the total US population. In addition to producing these high quality services and datasets the SAL has employed upwards of 350 students/recent graduates/new GIS professionals in the past 15 years and currently has a robust work force of 40 technicians/GIS operators.
The SAL would like to share some lessons learned and insights gained from decades of work in the geospatial field and running such a complex, flexible and high achieving organization in the rapidly changing technological landscape.
