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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-4/C7 tools/software) (Hay and Castilla, 2008) to generate GIS ready house polygons directly from the airborne thermal imagery rather than from pre-existing city cadastral datasets. This is critical when cadastral data are unavailable, thus allowing web accessible results within a week of data acquisition. (3) Web 2.0 compliant project development using Free and Open Source Software (FOSS) and OGC (Open Geospatial Consortium) standards for geospatial and location based services (Figure 1). We note that (i) Python (a dynamic object- oriented programming language) is used for rapid program development. (ii) PostgreSQL (an Object-Relational Database) is used with Python bindings, and (iii) PostGIS (a spatially enabled PostgreSQL) allows for the handling of geo-databases. (4) Incorporating volunteered geospatial information (i.e., citizen sensors) in to the HEAT web-service by requesting user information on roof material and house size (when not available from city cadastral data) that will allow for corrected emissivity to the walkability index developed by Walkscore (2010) which is used by millions. 4. DISCUSSION AND CONCLUSION Based on location-aware web services and high resolution airborne thermal imagery, the HEAT pilot project presents a Geomatics solution with a high potential for commercialization and advanced spatial decision making, that are applicable through a range of scales from the individual home owner, the neighbourhood, the community, to an entire city (Hay et al., 2010). Based on the ease and ubiquity of internet access, we forecast this project to provide community, commercial, and environmental utility on a global basis while promoting Canadian excellence in Thermal Imagery and location-aware web services. Figure 1. Yellow circles (Left) automatically define the 3 hottest locations on a home – which in this case fall above 4 sky-lights. The colored polygons (that overlay the image) are from a residential database, and are color based on the average roof-top temperature. This allows for neighborhoods to be evaluated at coarser scales. The mouse-over pointer value (3.12 oC) is dynamic, and the energy use-model estimates GHG and cost/day to heat this home to the maximum defined temp using different fuel types. When Natural Gas (in tan highlight) is double clicked, a model is generated (Right – Savings tab) for this home/year with total costs as well as GHG and savings based on reducing waste heat to the min roof temperature defined by the thermal sensor (prices shown are examples only). and green house gas estimates, thus tailoring individuals home waste-energy footprint information and potential savings (Figure 1). (5) The ability to define the three hottest locations on each home, and to compile the top 1000+ hottest homes in a city (Figure 2). (6) Monitoring may be applied yearly to provide evidence of successful Government and municipal energy incentive retrofit programs. (7) Multiscale analysis can also be conducted ranging from individual homes to neighborhoods, communities and cities, with opportunities to promote national and international intra- and inter-city heat waste competitions. (8) There is also potential to influence Calgary public energy use by developing a web-enabled Thermal Energy Index as part of Calgary’s public cadastral information. This will be similar To evaluate the current version of HEAT, please login as beta to (http://www.wasteheat.ca) with the password beta (no italics). Please note that this system is in continual development, so we invite you to provide us with your constructive comments and feedback and welcome collaborative opportunities and partnerships. REFERENCES Darby, S., 2006. The Effectiveness of Feedback on Energy Consumption. Report published by Environmental Change Institute. University of Oxford, pp 21. http://www.eci.ox.ac.uk/PDF Image | RESIDENTIAL WASTE HEAT ANALYSIS USING GEOBIA
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