GISB: A Benchmark for Geographic Map Information Extraction
The growing number of different models and approaches for Geographic Information Systems (GIS) brings high complexity when we want to develop new approaches and compare a new GIS algorithm. In order to test and compare different processing models and approaches, in a simple way, we identified the need of defining uniform testing methods, able to compare processing algorithms in terms of performance and accuracy regarding: large imaging processing, algorithms for GIS pattern-detection.
Taking into account, for instance, images collected during a drone flight or a satellite, it is important to know the processing cost to extract data when applying different processing models and approaches, as well as their accuracy (compare execution time vs. extracted data quality). In this work we propose a GIS Benchmark (GISB), a benchmark that allows to evaluate different approaches to detect/extract selected features from a GIS data-set. Considering a given data-set (or two data-sets, from different years, of the same region) it provides linear methods to compare different performance parameters regarding GIS information, making possible to access the most relevant information in terms of features and processing efficiency.
KeywordsBenchmark GIS Algorithms Spatial-temporal databases Bigdata Performance Experimentation Pattern-detection
This project is part of a larger software prototype, partially financed by CISUC research group from the University of Coimbra, and the Foundation for Science and Technology.
- 1.Aissi, S., Gouider, M.S.: Spatial and spatio-temporal multidimensional data modelling: A survey. arXiv preprint (2012). arXiv:1208.0163
- 2.Aji, A., Wang, F., Saltz, J.H.: Towards building a high performance spatial query system for large scale medical imaging data. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 309–318. ACM (2012)Google Scholar
- 4.Eldawy, A., Mokbel, M.F.: The era of big spatial data (2013)Google Scholar
- 6.Perumal, M., Velumani, B., Sadhasivam, A., Ramaswamy, K.: Spatial Data Mining Approaches for GIS– A Brief Review. In: Satapathy, S.C., Govardhan, A., Raju, K.S., Mandal, J.K. (eds.) Emerging ICT for Bridging the Future - Volume 2. AISC, vol. 338, pp. 579–592. Springer, Heidelberg (2014)Google Scholar