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Unmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution.
Umberto Andriolo; Gil Gonçalves; Nelson Rangel-Buitrago; Marco Paterni; Filipa Bessa; Luisa M.S. Gonçalves; Paula Sobral; Monica Bini; Diogo Duarte; Ángela Fontán-Bouzas; Diogo Gonçalves; Tomoya Kataoka; Marco Luppichini; Luis Pinto; Konstantinos Topouzelis; Anubis Vélez-Mendoza; Silvia Merlino. Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images. Marine Pollution Bulletin 2021, 169, 112542 .
AMA StyleUmberto Andriolo, Gil Gonçalves, Nelson Rangel-Buitrago, Marco Paterni, Filipa Bessa, Luisa M.S. Gonçalves, Paula Sobral, Monica Bini, Diogo Duarte, Ángela Fontán-Bouzas, Diogo Gonçalves, Tomoya Kataoka, Marco Luppichini, Luis Pinto, Konstantinos Topouzelis, Anubis Vélez-Mendoza, Silvia Merlino. Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images. Marine Pollution Bulletin. 2021; 169 ():112542.
Chicago/Turabian StyleUmberto Andriolo; Gil Gonçalves; Nelson Rangel-Buitrago; Marco Paterni; Filipa Bessa; Luisa M.S. Gonçalves; Paula Sobral; Monica Bini; Diogo Duarte; Ángela Fontán-Bouzas; Diogo Gonçalves; Tomoya Kataoka; Marco Luppichini; Luis Pinto; Konstantinos Topouzelis; Anubis Vélez-Mendoza; Silvia Merlino. 2021. "Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images." Marine Pollution Bulletin 169, no. : 112542.
Unmanned Aerial Systems (UAS, aka drones) are being used to map marine macro-litter on the coast. Within the UAS4Litter project, the application of UAS has been applied on three sandy beach-dune systems on the wave-dominated North Atlantic Portuguese coast. Several technical solutions have been tested in terms of drone mapping performance, manual image screening and marine litter map analysis. The conceptualization and implementation of a multidisciplinary framework allowed to improve and making more efficient the mapping of marine litter items with UAS on coastal environment.
The location of major marine litter loads within the monitored areas were found associated to beach slope and water level dynamics on the beach profiles. Moreover, the abundance of marine pollution was related to the geographical location and level of urbanization of the study sites. The testing of machine learning techniques underlined that automated technique returned reliable abundance map of marine litter, while manual image screening was required for a detailed categorization of the items.
As marine litter pollution on coastal dunes has received limited scientific attention when compared with sandy shores, a novel non-intrusive UAS-based marine litter survey have been also applied to quantify the level of contamination on coastal dunes. The results showed the influence of the different dune plant communities in trapping distinct type of marine litter, and the role played by wind and overwash events in defining the items pathways through the dune blowouts.
The experiences on the Portuguese coast show that UAS allows an integrated approach for marine litter mapping, beach morphodynamic and nearshore hydrodynamic, setting the ground for marine litter dynamic modelling on the shore. Besides, UAS can give a new impulse to coastal dune litter monitoring, where the long residence time of marine debris threat the bio-ecological equilibrium of these ecosystems.
Umberto Andriolo; Gil Gonçalves; Filipa Bessa; Paula Sobral; Luis Pinto; Diogo Duarte; Angela Fontán-Bouzas; Luisa Gonçalves. On the use of drones to detect and map marine macro-litter on the North Atlantic Portuguese beach-dune systems: the experiences of UAS4Litter project. 2021, 1 .
AMA StyleUmberto Andriolo, Gil Gonçalves, Filipa Bessa, Paula Sobral, Luis Pinto, Diogo Duarte, Angela Fontán-Bouzas, Luisa Gonçalves. On the use of drones to detect and map marine macro-litter on the North Atlantic Portuguese beach-dune systems: the experiences of UAS4Litter project. . 2021; ():1.
Chicago/Turabian StyleUmberto Andriolo; Gil Gonçalves; Filipa Bessa; Paula Sobral; Luis Pinto; Diogo Duarte; Angela Fontán-Bouzas; Luisa Gonçalves. 2021. "On the use of drones to detect and map marine macro-litter on the North Atlantic Portuguese beach-dune systems: the experiences of UAS4Litter project." , no. : 1.
Non-destructive testing (NDT) techniques play an important role in the characterization and diagnosis of historic buildings, keeping in mind their conservation and possible rehabilitation. This paper presents a new approach that merges building information modeling (BIM) with environment geospatial data obtained by several non-destructive techniques, namely terrestrial laser scanning, ground-penetrating radar, infrared thermography, and the automatic classification of pathologies based on RGB (red, green, blue) imaging acquired with an unmanned aircraft system (UAS). This approach was applied to the inspection of the Monastery of Batalha in Leiria, Portugal, a UNESCO World Heritage Site. To assess the capabilities of each technique, different parts of the monastery were examined, namely (i) part of its west façade, including a few protruding buttresses, and (ii) the masonry vaults of the Church (nave, right-hand aisle, and transept) and the Founder’s Chapel. After describing the employed techniques, a discussion of the optimization, treatment and integration of the acquired data through the BIM approach is presented. This work intends to contribute to the application of BIM in the field of cultural heritage, aiming at its future use in different activities such as facility management, support in the restoration and rehabilitation process, and research.
Mercedes Solla; Luisa Gonçalves; Gil Gonçalves; Carina Francisco; Iván Puente; Paulo Providência; Florindo Gaspar; Hugo Rodrigues. A Building Information Modeling Approach to Integrate Geomatic Data for the Documentation and Preservation of Cultural Heritage. Remote Sensing 2020, 12, 4028 .
AMA StyleMercedes Solla, Luisa Gonçalves, Gil Gonçalves, Carina Francisco, Iván Puente, Paulo Providência, Florindo Gaspar, Hugo Rodrigues. A Building Information Modeling Approach to Integrate Geomatic Data for the Documentation and Preservation of Cultural Heritage. Remote Sensing. 2020; 12 (24):4028.
Chicago/Turabian StyleMercedes Solla; Luisa Gonçalves; Gil Gonçalves; Carina Francisco; Iván Puente; Paulo Providência; Florindo Gaspar; Hugo Rodrigues. 2020. "A Building Information Modeling Approach to Integrate Geomatic Data for the Documentation and Preservation of Cultural Heritage." Remote Sensing 12, no. 24: 4028.
Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.
Gil Gonçalves; Umberto Andriolo; Luísa Gonçalves; Paula Sobral; Filipa Bessa. Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods. Remote Sensing 2020, 12, 2599 .
AMA StyleGil Gonçalves, Umberto Andriolo, Luísa Gonçalves, Paula Sobral, Filipa Bessa. Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods. Remote Sensing. 2020; 12 (16):2599.
Chicago/Turabian StyleGil Gonçalves; Umberto Andriolo; Luísa Gonçalves; Paula Sobral; Filipa Bessa. 2020. "Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods." Remote Sensing 12, no. 16: 2599.
The role of values in climate-related decision-making is a prominent theme of climate communication research. The present study examines whether forest professionals are more driven by values than scientists are, and if this results in value polarization. A questionnaire was designed to elicit and assess the values assigned to expected effects of climate change by forest professionals and scientists working on forests and climate change in Europe. The countries involved covered a north-to-south and west-to-east gradient across Europe, representing a wide range of bio-climatic conditions and a mix of economic–social–political structures. We show that European forest professionals and scientists do not exhibit polarized expectations about the values of specific impacts of climate change on forests in their countries. In fact, few differences between forest professionals and scientists were found. However, there are interesting differences in the expected values of forest professionals with regard to climate change impacts across European countries. In Northern European countries, the aggregated values of the expected effects are more neutral than they are in Southern Europe, where they are more negative. Expectations about impacts on timber production, economic returns, and regulatory ecosystem services are mostly negative, while expectations about biodiversity and energy production are mostly positive.
Johannes Persson; Kristina Blennow; Luísa Gonçalves; Alexander Borys; Ioan Dutcă; Jari Hynynen; Emilia Janeczko; Mariyana Lyubenova; Simon Martel; Jan Merganic; Katarína Merganičová; Mikko Peltoniemi; Michal Petr; Fernando H. Reboredo; Giorgio Vacchiano; Christopher P.O. Reyer. No polarization–Expected Values of Climate Change Impacts among European Forest Professionals and Scientists. Sustainability 2020, 12, 2659 .
AMA StyleJohannes Persson, Kristina Blennow, Luísa Gonçalves, Alexander Borys, Ioan Dutcă, Jari Hynynen, Emilia Janeczko, Mariyana Lyubenova, Simon Martel, Jan Merganic, Katarína Merganičová, Mikko Peltoniemi, Michal Petr, Fernando H. Reboredo, Giorgio Vacchiano, Christopher P.O. Reyer. No polarization–Expected Values of Climate Change Impacts among European Forest Professionals and Scientists. Sustainability. 2020; 12 (7):2659.
Chicago/Turabian StyleJohannes Persson; Kristina Blennow; Luísa Gonçalves; Alexander Borys; Ioan Dutcă; Jari Hynynen; Emilia Janeczko; Mariyana Lyubenova; Simon Martel; Jan Merganic; Katarína Merganičová; Mikko Peltoniemi; Michal Petr; Fernando H. Reboredo; Giorgio Vacchiano; Christopher P.O. Reyer. 2020. "No polarization–Expected Values of Climate Change Impacts among European Forest Professionals and Scientists." Sustainability 12, no. 7: 2659.
The aim of this article is to assess if the data provided by soft classifiers and uncertainty measures can be used to identify regions with different levels of accuracy in a classified image. To this aim a soft Bayesian classifier was used, which enables the assignment of classifications confidence levels to all pixels. Two uncertainty measures were also used, namely the Relative Maximum Deviation (RMD) uncertainty measure and the Normalized Entropy (NE). The approach was tested on a case study. A multispectral IKONOS image was classified and the classification uncertainty and confidence where computed and analysed. Regions with different levels of uncertainty and confidence were identified. Reference datasets were then used to assess the classification accuracy of the whole study area and also in the regions with different levels of uncertainty and confidence. A comparative analysis was made on the variation of accuracy and classification uncertainty and confidence along the map and per class. The results show that for the regions with more uncertainty or less confidence the spatially constrained confusion matrices always generate lower values of global accuracy than for global accuracy of the regions with less uncertainty or more confidence. The analysis of the user’s and producer’s accuracy also shows the same general tendency. Proposals are then made on methodologies to use the information provided by the uncertainty and confidence to identify less reliable regions and also to improve classification results using fully automated approaches.
C. C. Fonte; L. M. S. Gonçalves. IDENTIFICATION OF LOW ACCURACY REGIONS IN LAND COVER MAPS USING UNCERTAINTY MEASURES AND CLASSIFICATION CONFIDENCE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, XLII-4, 201 -208.
AMA StyleC. C. Fonte, L. M. S. Gonçalves. IDENTIFICATION OF LOW ACCURACY REGIONS IN LAND COVER MAPS USING UNCERTAINTY MEASURES AND CLASSIFICATION CONFIDENCE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; XLII-4 ():201-208.
Chicago/Turabian StyleC. C. Fonte; L. M. S. Gonçalves. 2018. "IDENTIFICATION OF LOW ACCURACY REGIONS IN LAND COVER MAPS USING UNCERTAINTY MEASURES AND CLASSIFICATION CONFIDENCE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4, no. : 201-208.
Luisa M. S. Gonçalves; Hugo Rodrigues; Florindo Gaspar. Nondestructive Techniques for the Assessment and Preservation of Historic Structures. Nondestructive Techniques for the Assessment and Preservation of Historic Structures 2017, 1 .
AMA StyleLuisa M. S. Gonçalves, Hugo Rodrigues, Florindo Gaspar. Nondestructive Techniques for the Assessment and Preservation of Historic Structures. Nondestructive Techniques for the Assessment and Preservation of Historic Structures. 2017; ():1.
Chicago/Turabian StyleLuisa M. S. Gonçalves; Hugo Rodrigues; Florindo Gaspar. 2017. "Nondestructive Techniques for the Assessment and Preservation of Historic Structures." Nondestructive Techniques for the Assessment and Preservation of Historic Structures , no. : 1.
J. Cunha; L.M.S. Gonçalves; F. Aguilera; P.A. Roldan; Cláudia Pinto. Subsurface dynamic evaluation to identify old quarries in urban areas: Lisbon city case study. Procedia Engineering 2017, 209, 195 -201.
AMA StyleJ. Cunha, L.M.S. Gonçalves, F. Aguilera, P.A. Roldan, Cláudia Pinto. Subsurface dynamic evaluation to identify old quarries in urban areas: Lisbon city case study. Procedia Engineering. 2017; 209 ():195-201.
Chicago/Turabian StyleJ. Cunha; L.M.S. Gonçalves; F. Aguilera; P.A. Roldan; Cláudia Pinto. 2017. "Subsurface dynamic evaluation to identify old quarries in urban areas: Lisbon city case study." Procedia Engineering 209, no. : 195-201.
To predict the degradation of concrete structures is extremely challenging. The typical approach combines periodic visual inspections with required non-destructive tests. However, this methodology only discretely evaluates few areas of the structure, being also time consuming and subject to human error. Therefore, a new method designated ‘automatic concrete health monitoring’ is herein presented which aims at automatically characterising and monitoring the state of conservation of concrete surfaces by combining photogrammetry, image processing and multi-spectral analysis. The method was designed to (i) characterise crack pattern, displacement and strain fields; (ii) map damages and (iii) assess and define restoration tasks.
Jónatas Valença; Daniel Dias-Da-Costa; Luísa Gonçalves; Eduardo Júlio; Helder Araujo. Automatic concrete health monitoring: assessment and monitoring of concrete surfaces. Structure and Infrastructure Engineering 2013, 10, 1547 -1554.
AMA StyleJónatas Valença, Daniel Dias-Da-Costa, Luísa Gonçalves, Eduardo Júlio, Helder Araujo. Automatic concrete health monitoring: assessment and monitoring of concrete surfaces. Structure and Infrastructure Engineering. 2013; 10 (12):1547-1554.
Chicago/Turabian StyleJónatas Valença; Daniel Dias-Da-Costa; Luísa Gonçalves; Eduardo Júlio; Helder Araujo. 2013. "Automatic concrete health monitoring: assessment and monitoring of concrete surfaces." Structure and Infrastructure Engineering 10, no. 12: 1547-1554.
Jónatas Valença; Luisa Gonçalves; Eduardo Julio. Damage assessment on concrete surfaces using multi-spectral image analysis. Construction and Building Materials 2013, 40, 971 -981.
AMA StyleJónatas Valença, Luisa Gonçalves, Eduardo Julio. Damage assessment on concrete surfaces using multi-spectral image analysis. Construction and Building Materials. 2013; 40 ():971-981.
Chicago/Turabian StyleJónatas Valença; Luisa Gonçalves; Eduardo Julio. 2013. "Damage assessment on concrete surfaces using multi-spectral image analysis." Construction and Building Materials 40, no. : 971-981.
The production of thematic maps from remotely sensed images requires the application of classification methods. A great variety of classifiers are available, producing frequently considerably different results. Therefore, the automatic extraction of thematic information requires the choice of the most appropriate classifier for each application. One of the main objectives of the research described in this article is to evaluate the performance of supervised classifiers using the information provided by the application of uncertainty measures to the testing sets, instead of statistical accuracy indices. The second main objective is to show that the information provided by the uncertainty measures for the training set may be used to assess and redefine the sample sites included in this set, in order to improve the classification results. To achieve the proposed objectives, two supervised classifiers, one probabilistic and another fuzzy, were applied to a very high spatial resolution (VHSR) image. The results show that similar conclusions on the classifiers’ performance are obtained with the uncertainty measures and the traditional accuracy indices obtained from error matrices. It is also shown that the redefinition of the training set based on the information provided by the uncertainty measures may generate more accurate outputs.
Luisa M. S. Gonçalves; Cidália C. Fonte; Eduardo N. B. S. Júlio; Mario Caetano. The application of uncertainty measures in the training and evaluation of supervised classifiers. International Journal of Remote Sensing 2011, 33, 2851 -2867.
AMA StyleLuisa M. S. Gonçalves, Cidália C. Fonte, Eduardo N. B. S. Júlio, Mario Caetano. The application of uncertainty measures in the training and evaluation of supervised classifiers. International Journal of Remote Sensing. 2011; 33 (9):2851-2867.
Chicago/Turabian StyleLuisa M. S. Gonçalves; Cidália C. Fonte; Eduardo N. B. S. Júlio; Mario Caetano. 2011. "The application of uncertainty measures in the training and evaluation of supervised classifiers." International Journal of Remote Sensing 33, no. 9: 2851-2867.
The aim of this paper was to investigate the usefulness of non-specificity uncertainty measures to evaluate soft classifications of remote sensing images. In particular, we analysed whether these measures could be used to identify the difficulties found by the classifier and to estimate the classification accuracy. Two non-specificity uncertainty measures were considered, the non-specificity measure (NSp) and the U-uncertainty measure, and their behaviour was analysed to evaluate which is the most appropriate for this application. To overcome the fact that these two measures have different ranges, a normalized version (Un) of the U-uncertainty measure was used. Both measures were applied to evaluate the uncertainty of a soft classification of a very high spatial resolution multispectral satellite image, performed with an object-oriented image analysis based on a fuzzy classification. The classification accuracy was evaluated using an error matrix and the user's and producer's accuracies were computed. Two uncertainty indexes are proposed for each measure, and the correlation between the information given by them and the user's and producer's accuracies was determined to assess the relationship and compatibility of both sources of information. The results show that there is a positive correlation between the information given by the uncertainty and accuracy indexes, but mainly between the uncertainty indexes and the user's accuracy, where the correlation achieved 77%. This study shows that uncertainty indexes may be used, along with the possibility distributions, as indicators of the classification performance, and may therefore be very useful tools.
Luisa M. S. Gonçalves; Cidália C. Fonte; Eduardo N. B. S. Júlio; Mario Caetano. Evaluation of soft possibilistic classifications with non-specificity uncertainty measures. International Journal of Remote Sensing 2010, 31, 5199 -5219.
AMA StyleLuisa M. S. Gonçalves, Cidália C. Fonte, Eduardo N. B. S. Júlio, Mario Caetano. Evaluation of soft possibilistic classifications with non-specificity uncertainty measures. International Journal of Remote Sensing. 2010; 31 (19):5199-5219.
Chicago/Turabian StyleLuisa M. S. Gonçalves; Cidália C. Fonte; Eduardo N. B. S. Júlio; Mario Caetano. 2010. "Evaluation of soft possibilistic classifications with non-specificity uncertainty measures." International Journal of Remote Sensing 31, no. 19: 5199-5219.
The classification of remote sensing images performed with different classifiers usually produces different results. The aim of this paper is to investigate whether the outputs of different soft classifications may be combined to increase the classification accuracy, using the uncertainty information to choose the best class to assign to each pixel. If there is disagreement between the outputs obtained with the several classifiers, the proposed method selects the class to assign to the pixel choosing the one that presents less uncertainty. The proposed approach was applied to an IKONOS image, which was classified using two supervised soft classifiers, the Multi-layer Perceptron neural network classifier and a fuzzy classifier based on the underlying logic of the Minimum-Distance-to-Means. The overall accuracy of the classification obtained with the combination of both classifications with the proposed methodology was higher than the overall accuracy of the original classifications, which shows that the methodology is promising and may be used to increase classification accuracy.
Luisa M. S. Gonçalves; Cidália C. Fonte; Mario Caetano. Using Uncertainty Information to Combine Soft Classifications. Computer Vision 2010, 6178, 455 -463.
AMA StyleLuisa M. S. Gonçalves, Cidália C. Fonte, Mario Caetano. Using Uncertainty Information to Combine Soft Classifications. Computer Vision. 2010; 6178 ():455-463.
Chicago/Turabian StyleLuisa M. S. Gonçalves; Cidália C. Fonte; Mario Caetano. 2010. "Using Uncertainty Information to Combine Soft Classifications." Computer Vision 6178, no. : 455-463.
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 pixel-based 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.
Luisa M.S. Gonçalves; Cidália Fonte; Eduardo Julio; Mario Caetano. A method to incorporate uncertainty in the classification of remote sensing images. International Journal of Remote Sensing 2009, 30, 5489 -5503.
AMA StyleLuisa M.S. Gonçalves, Cidália Fonte, Eduardo Julio, Mario Caetano. A method to incorporate uncertainty in the classification of remote sensing images. International Journal of Remote Sensing. 2009; 30 (20):5489-5503.
Chicago/Turabian StyleLuisa M.S. Gonçalves; Cidália Fonte; Eduardo Julio; Mario Caetano. 2009. "A method to incorporate uncertainty in the classification of remote sensing images." International Journal of Remote Sensing 30, no. 20: 5489-5503.
Luisa Gonçalves; Cidália Fonte; Eduardo Julio; Mario Caetano. Assessment of the state of conservation of buildings through roof mapping using very high spatial resolution images. Construction and Building Materials 2009, 23, 2795 -2802.
AMA StyleLuisa Gonçalves, Cidália Fonte, Eduardo Julio, Mario Caetano. Assessment of the state of conservation of buildings through roof mapping using very high spatial resolution images. Construction and Building Materials. 2009; 23 (8):2795-2802.
Chicago/Turabian StyleLuisa Gonçalves; Cidália Fonte; Eduardo Julio; Mario Caetano. 2009. "Assessment of the state of conservation of buildings through roof mapping using very high spatial resolution images." Construction and Building Materials 23, no. 8: 2795-2802.
Luísa Gonçalves; Ciddlia Fonte; Eduardo Júlio; Mario Caetano. Evaluation of Remote Sensing Image Classifiers with Uncertainty Measures. Spatial Data Quality 2009, 163 -177.
AMA StyleLuísa Gonçalves, Ciddlia Fonte, Eduardo Júlio, Mario Caetano. Evaluation of Remote Sensing Image Classifiers with Uncertainty Measures. Spatial Data Quality. 2009; ():163-177.
Chicago/Turabian StyleLuísa Gonçalves; Ciddlia Fonte; Eduardo Júlio; Mario Caetano. 2009. "Evaluation of Remote Sensing Image Classifiers with Uncertainty Measures." Spatial Data Quality , no. : 163-177.