What You See is What You Map: Geometry-Preserving Micro-Mapping for Smaller Geographic Objects with mapIT
Geographic information is increasingly contributed by volunteers via crowdsourcing platforms. However, most tools and methods require a high technical affinity of its users and a good understanding of geographic classification systems. These technological and educational barriers prevent casual users to contribute spatial data. In this chapter we present mapIT, a method to acquire and contribute complex geographic data. We further introduce the concept of micro-mapping, the acquisition of geometrically correct geometric data of small geographic entities. mapIT is a method for micro-mapping with smartphones with high geometric precision. We show that mapIT is highly accurate and able to reconstruct the geometry of mapped entities correctly.Please check and confirm the author names and initials are correct.
KeywordsGeographic Data Geospatial Data Volunteer Geographic Information Spatial Entity Tilt Sensor
We gratefully acknowledge support by the University of Bremen, the German Research Foundation (DFG) within the SFB/TR8 Spatial Cognition and the International Research Training Group on Semantic Integration of Geospatial Information (GRK 1498), and support by the European Union Seventh Framework Programme—Marie Curie Actions, Initial Training Network GEOCROWD under grant agreement No. FP7-PEOPLE-2010-ITN-264994.
- Asyraf Hamdani M, Hashim K, Adnan R, Manan Samad A (2011) 3D images processing of structural building using digital close-range photogrammetric approach. In: IEEE 7th international colloquium on signal processing and its applications (CSPA), pp 318–321Google Scholar
- Biagioni J, Eriksson J (2012) Map inference in the face of noise and disparity. In: Proceedings of the 20th international conference on advances in geographic information systems (ACM SIGSPATIAL GIS 2012), Redondo Beach, CaliforniaGoogle Scholar
- Brando C, Bucher B, Abadie N (2011) Specifications for user generated spatial content. Adv Geoinf Sci Chang World, pp 479–495Google Scholar
- Douglas D, Peucker T (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer, 10(2):112–172Google Scholar
- Foley J, van Dam A, Feiner S, Hughes J, Phillips R (1993) Introduction to computer graphics. Addison-Wesley ProfessionalGoogle Scholar
- Fraser C, Cronk S, Hanley H (2008) Close-range photogrammetry in traffic incident management. In: Proceedings of XXI ISPRS congress commission V, WG V, Citeseer, vol 1, pp 125–128Google Scholar
- Frommberger L, Schmid F, Cai C, Freksa C, Haddawy P (2012) Barrier-free micro-mapping for development and poverty reduction. Role of volunteered geographic information in advancing science: quality and credibilityGoogle Scholar
- Maisonneuve N, Chopard B (2012) Crowdsourcing satellite imagery analysis: study of parallel and iterative models. Geograph Inf Sci, pp 116–131Google Scholar
- Ramm F, Topf J, Chilton S (2010) OpenStreetMap—using and enhancing the free map of the world, UIT CambridgeGoogle Scholar
- Schmid F, Cai C, Frommberger L (2012) A new micro-mapping method for rapid vgi-ing of small geographic features. In: Geographic information science: 7th international conference (GIScience, (2012) Columbus, Ohio, USAGoogle Scholar
- Schmid F, Kutz O, Frommberger L, Kauppinen T, Cai C (2012) Intuitive and natural interfaces for geospatial data classification. In: Workshop on place-related knowledge acquisition research (P-KAR), Kloster Seeon, GermanyGoogle Scholar
- Turner AJ (2006) Introduction to neogeography. O’Reilly, MediaGoogle Scholar