Comparative study of Land Use/Cover classification using Flickr photos, satellite imagery and Corine land cover database

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Volunteered Geographic Information has been increasing exponentially over the last years, capturing the attention of the scientific community. Researchers have been very active exploring a vast amount of initiatives and trying to develop methodologies and possible real applications for this new source of geographic information. Land Use/Cover production is one of the areas where this type of geographic information might be very useful. In this paper we evaluate if geo-referenced and publicly available photos from the Flickr initiative can be used as a source of geographic information to help Land Use/Cover classification. Using the Corine Land Cover nomenclature, we compare the classification obtained for selected photo locations, against the classification obtained from high resolution satellite imagery for the same locations. We conclude that this source cannot be used alone for the purpose of Land Use/Cover classification but we also believe that it might contain helpful information if combined with other sources

Assessing positional accuracy of drainage networks extracted from ASTER, SRTM and OpenStreetMap

This study intends to evaluate the positional accuracy and compare the completeness of the drainage networks extracted from three sources of free geographic data, namely from the Digital Elevation Models ASTER and SRTM and the collaborative project OpenStreetMap (OSM), in an area included in the basin of Mondego river, located in the centre of continental Portugal. The drainage networks extracted from ASTER and SRTM are generated considering several values of flow accumulation as the critical level to identify the water courses and the feature “waterway” was extracted from OSM. To assess the completeness and positional accuracy of these water courses the drainage network of the 1/25000 topographic map of the Portuguese Army Geographical Institute was used as reference. The distance between the ASTER, SRTM and OSM derived water courses to the reference data was computed as well as the length of the water courses and the results compared

How volunteered geographic information can be integrated into emergency management practice? : first lessons learned from an urban fire simulation in the city of Coimbra

In the past few years, volunteered geographic information (VGI) has emerged as a new resource for improving the management of emergencies. Despite the growing body of research dedicated to the use of VGI in crisis management, studies are still needed that systematically investigate the incorporation of VGI into practical emergency management. To fill this gap, this paper proposes a research design for investigating and planning the incorporation of VGI into work practices and decision-making of emergency agencies by means of simulation exercises. Furthermore, first lessons are drawn from a field study performed within a simulation exercise of an urban fire in Coimbra, Portugal, implemented together with local civil protection agents. Emergency management practitioners identified a high potential in the pictures taken in-situ by volunteers for improving situational awareness and supporting decision-making. They also pointed out to challenges associated to processing VGI and filtering high-value information in real-time

A method to incorporate uncertainty in the classification of remote sensing images

The aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following steps: (1) a pixel-based hard classification with a probabilistic Bayesian classifier; (2) computation of the posterior probabilities and quantification of the classification uncertainty using an uncertainty measure; (3) image segmentation and (4) object classification based on decision rules. The classification of the resulting objects into LUs is performed considering a set of decision rules that incorporate the pixelbased classification uncertainty. The proposed methodology was tested on the classification of an IKONOS satellite image. The accuracy of the classification was computed using an error matrix. The comparison between the results obtained with the proposed approach and those obtained without considering the classification uncertainty revealed a 12% increase in the overall accuracy. This shows that the information about uncertainty can be valuable when making decisions and can actually increase the accuracy of the classification results.info:eu-repo/semantics/publishedVersio

Assessment of the state of conservation of buildings through roof mapping using very high spatial resolution images

The assessment of the state of conservation of buildings is extremely important in urban rehabilitation. In the case of historical towns or city centres, the pathological characterization using traditional methods is a laborious and time consuming procedure. This study aims to show that Very High Spatial Resolution (VHSR) multispectral images can be used to obtain information regarding the state of conservation of roofs where, usually, building degradation starts. The study was performed with multispectral aerial images with a spatial resolution of 0.5 m. To extract the required information, a hybrid classification method was developed, that integrates pixel and object based classification methods, as well as information regarding the classification uncertainty. The proposed method was tested on the classification of the historical city centre of Coimbra, in Portugal, that includes over than 800 buildings. The results were then validated with the data obtained from a study conducted during 2 years by a nine element team from the University of Coimbra, using traditional methods. The study performed achieved a global classification accuracy of 78%, which proves that the state of conservation of roofs can be obtained from VHSR multispectralimages using the described methodology with a fairly good accuracy.info:eu-repo/semantics/publishedVersio

Usability of VGI for validation of land cover maps

Volunteered Geographic Information (VGI) represents a growing source of potentially valuable data for many applications, including land cover map validation. It is still an emerging field and many different approaches can be used to take value from VGI, but also many pros and cons are related to its use. Therefore, since it is timely to get an overview of the subject, the aim of this article is to review the use of VGI as reference data for land cover map validation. The main platforms and types of VGI that are used and that are potentially useful are analysed. Since quality is a fundamental issue in map validation, the quality procedures used by the platforms that collect VGI to increase and control data quality are reviewed and a framework for addressing VGI quality assessment is proposed. A review of cases where VGI was used as an additional data source to assist in map validation is made, as well as cases where only VGI was used, indicating the procedures used to assess VGI quality and fitness for use. A discussion and some conclusions are drawn on best practices, future potential and the challenges of the use of VGI for land cover map validation

Automatic Extraction and Filtering of OpenStreetMap Data to Generate Training Datasets for Land Use Land Cover Classification

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

Automatic Extraction and Filtering of OpenStreetMap Data to Generate Training Datasets for Land Use Land Cover Classification

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. Di erent 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 o cial “Carta de Ocupação do Solo” (COS). The methodology was applied to two study areas with di erent 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