This paper tests an automated methodology for generating training data from OpenStreetMap (OSM) to classify Sentinel-2 imagery into Land Use/Land Cover (LULC) classes. Different sets of training data were generated and used as inputs for the image classification. Firstly, OSM data was converted into LULC maps using the OSM2LULC_4T software package. The Random Forest classifier was then trained to classify a time-series of Sentinel-2 imagery into 8 LULC classes with samples extracted from: (1) The LULC maps produced by OSM2LULC_4T (TD0); (2) the TD1 dataset, obtained after removing mixed pixels from TD0; (3) the TD2 dataset, obtained by filtering TD1 using radiometric indices. The classification results were generalized using a majority filter and hybrid maps were created by merging the classification results with the OSM2LULC outputs. The accuracy of all generated maps was assessed using the 2018 official “Carta de Ocupação do Solo” (COS). The methodology was applied to two study areas with different characteristics. The results show that in some cases the filtering procedures improve the training data and the classification results. This automated methodology allowed the production of maps with overall accuracy between 55% and 78% greater than that of COS, even though the used nomenclature includes classes that can be easily confused by the classifiers.

Abstract. Synoptic remote sensing systems have been broadly used within supervised classification methods to map land use and land cover (LULC). Such methods rely on high quality sets of training data that are able to characterize the target classes. Often, training data is manually generated, either by field campaigns and/or by photointerpretation of ancillary remote sensing imagery. Several authors already proposed methodologies to attenuate such labour-intensive task of generating training data. One of the preferred datasets that are used as input training data is OpenStreetMap (OSM), which aims at creating a publicly available vector map of the world with the input of volunteers. However, OSM data is spatially heterogenous (e.g., capital cities and highly populated areas often have high degrees of completion while unpopulated regions often have a lower degree of completion), where there are still large areas without OSM coverage. In this paper we present a set of experiments that aim at assessing the geographical transferability of satellite image-based segmentation models trained with OSM derived data. To this end, we chose two locations with different OSM coverage and disparate landscape (metropolitan region vs natural park region, in different landscape units), and assess how these models behave when trained in a region and applied in the other. The results show that the mapping of some classes is improved when considering a model trained in a different location.

The FireLoc system aims at geolocating forest fires observed by the citizens using data uploaded by the citizens using a dedicated app developed for mobile devices. The collected data includes the location of the observer (determined with the Global Navigation Satellite System receiver embedded in the device), the magnetic bearing registered by the mobile device when facing the fire. However, due to the errors that may be associated with the measurement of the magnetic bearing an additional measurement is collected with the app, which is the magnetic bearing measured when the volunteer is facing his/her shadow. Even though the collection of this data is not mandatory at the current version of the app to upload a contribution, it may be very useful to estimate the error associated with the measurement of the magnetic bearing. This short paper describes the process to determine the location of the observed fire with the collected data, without considering the error associated with the measurement of the orientation and when this error is considered along with a fuzzy approach to assess the region where the fire is more likely to be located.