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 image processing, algorithms for GIS pattern-detection.   Taking into account, for instance, images collected during a done 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 (GPII), a benchmark that allows evaluating different approaches to detect/extract selected features from a GIS dataset. Considering a given dataset (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. Moreover, our approach to test algorithms makes possible to change the data-set in order to support different purpose algorithms.
['Pedro Martins', 'José Cecílio', 'Maryam Abbasi', 'Pedro Furtado']
GPII: A Benchmark for Generic Purpose Image Information