Urban land cover mapping using medium spatial resolution satellite imageries: effectiveness of Decision Tree Classifier

The study is inserted in the framework of information extraction from satellite imageries for supporting rapid mapping activities, where information need to be extracted quickly and the elimination, also if partially, of manual digitalization procedures, can be considered a great breakthrough. The main aim of this study was therefore to develop algorithms for the extraction of urban layer by means of medium spatial resolution Landsat data processing; Decision Tree classifier was investigated as classification techniques, thus it allows to extract rules that can be later applied to different scenes. In particular, the aim was to evaluate which steps to perform in order to obtain a good classification procedure, mainly focusing on processing that can be applied to images and on training set features. The training set was evaluated on the basis of the number of classes to use for its creation, together with the temporal extension of the training set and input attributes, while images were submitted to different kind of radiometric pre and post-processing. The aim was the evaluation of the best variables to set for the creation of the training set, to be used for the classifier generation. Above-mentioned variables were compared and results evaluated on the basis of reached accuracies. Data used for the validation were derived from the Digital Regional Technical Ma

Urban detection using Decision Tree classifier: a case study

This work constitutes a first step towards the definition of a methodology for automatic urban extraction from medium spatial resolution Landsat data. Decision Tree is investigated as classification technique due to its ability in establishing which is the most relevant information to be used for the classification process and its capability of extracting rules that can be further ap-plied to other inputs. The attention was focused on the evaluation of parameters that better define the training set to be used for the learning phase of the classifier since its definition affects all the next steps of the process. Different training sets were created by combining different features, such as different level of radiometric pre-processing applied to the input images, the number of classes considered to train the classifier, the temporal extent of the training set and the use of different at-tributes (bands or spectral indexes). Different post-processing techniques were also evaluated. Classifiers, obtained by the generated training sets, were evaluated in two different areas of Pied-mont Region, where the official regional cartography at scale 1:10000 was used for validation. Accuracies round 81% in the Torino case study and around 96%-97% in Asti case study were reached, thanks to the use of indexes such as NDVI and NDBBBI and the use of post-processing such as majority filtering that allowed enhancing classifier performance

Utilizzo di Alberi Decisionali per la classificazione di aree urbanizzate

The paper describes a preliminary study on the urban classification accuracies obtained by means of the Decision Tree classifier. The study was conducted over the area of Turin (Italy), with Landsat ETM+ imagery and with an official regional map (Cartografia Tecnica Regionale) used as ground truth. In particular the variation of the accuracies was evaluated, depending on the changing of the algorithm input attributes such as the level of applied radiometric pre-processing, the considered number of classes, the temporal extent of the training set and the use of spectral indexes. Results show that overall accuracies of 80% can be achieved and that spectral indexes are the type of attribute that affect most these accuracies

Utilizzo di Alberi Decisionali per la classificazione di aree urbanizzate

The paper describes a preliminary study on the urban classification accuracies obtained by means of the Decision Tree classifier. The study was conducted over the area of Turin (Italy), with Landsat ETM+ imagery and with an official regional map (Cartografia Tecnica Regionale) used as ground truth. In particular the variation of the accuracies was evaluated, depending on the changing of the algorithm input attributes such as the level of applied radiometric pre-processing, the considered number of classes, the temporal extent of the training set and the use of spectral indexes. Results show that overall accuracies of 80% can be achieved and that spectral indexes are the type of attribute that affect most these accuracie

Base cartography for land and water management in Sub-Saharan Africa

Base cartography at proper scale for land and water management is rarely available in Least Developed Countries (LDCs). Despite the massive presence of international cooperation programs and projects carried out in various LDCs, a low budget is usually allocated for base data retrieval, which could be helpful for a wide range of on-site actions. A food security project in Burkina Faso, aiming at increasing the agricultural production through supporting farmers' unions, is herein used as a case study. In this framework update cartography at large scale was needed in order to plan Soil and Water Conservation (SWC) interventions at catchment scale. However, best existing official maps, dated 1984, were at 1:50.000 scale, which is a highly coarse detail level to intervene at large scales. Data at higher resolution were available at the national cartographic institute, obtained from aerial surveys performed in the last decade. Aerial imagery allowed then to perform feature extraction over the areas of interest, thus updating the existing cartography and making it suitable for land and water management plannin