Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

    Records 1 - 14 / 14

    • help
    • print

      Print search results

    • export

      Export search results

    Check title to add to marked list
    Data and code of the paper "Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient" submitted to IJGIS
    Vargas Munoz, John ; Tuia, Devis ; Falcão, Alexandre X. - \ 2020
    University of Campinas
    image annotation algorithms - OpenStreetMap data
    Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient
    Vargas Muñoz, John E. ; Tuia, Devis ; Falcão, Alexandre X. - \ 2020
    International Journal of Geographical Information Science (2020). - ISSN 1365-8816 - 21 p.
    Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas. Recent efforts have tackled this problem as the detection of buildings in aerial images. However, the quality and the amount of rural building annotated data in open mapping services like OpenStreetMap (OSM) is not sufficient for training accurate models for such detection. Although these methods have the potential of aiding in the update of rural building information, they are not accurate enough to automatically update the rural building maps. In this paper, we explore a human-computer interaction approach and propose an interactive method to support and optimize the work of volunteers in OSM. The user is asked to verify/correct the annotation of selected tiles during several iterations and therefore improving the model with the new annotated data. The experimental results, with simulated and real user annotation corrections, show that the proposed method greatly reduces the amount of data that the volunteers of OSM need to verify/correct. The proposed methodology could benefit humanitarian mapping projects, not only by making more efficient the process of annotation but also by improving the engagement of volunteers.
    OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing
    Vargas Munoz, John E. ; Srivastava, Shivangi ; Tuia, Devis ; Falcao, Alexandre X. - \ 2020
    IEEE Geoscience and Remote Sensing Magazine (2020). - ISSN 2473-2397
    Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data
    Srivastava, Shivangi ; Vargas Muñoz, John E. ; Lobry, Sylvain ; Tuia, Devis - \ 2020
    International Journal of Geographical Information Science 34 (2020)6. - ISSN 1365-8816 - p. 1117 - 1136.
    We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization.
    A new environmental governance
    Delgado, Luisa E. ; Zorondo-Rodríguez, Francisco ; Bachmann-Vargas, Pamela ; Soto, Carmiña ; Avila Foucat, Veronique S. ; Gutierrez, Ricardo A. ; Muñoz-Barriga, Andrea ; Ferreiro, Oscar E. - \ 2019
    In: Social-ecological Systems of Latin America / Delgado, Luisa E., Marin, Victor H., Springer International Publishing - ISBN 9783030284510 - p. 117 - 135.
    Adaptation - Complexity - Environmental governance - Latin America - Public policies - Social-ecological systems
    At present, there is no unified theoretical framework to deal with environmental governance issues. Consequently, there is a diversity of interpretations of the concept at the public-political arena both nationally and internationally. Recent Latin American efforts have given a step forward conceptualizing environmental governance from the South and systematizing experiences to illustrate a diverse contemporaneous reality. At a regional scale, within the last decades, discursive turns in national policies such as the introduction of the sustainable development concept have triggered an increase in studies and applications of environmental governance (e.g., forest's governance, climate change, marine coastal zones) including the use of the ecosystem services concept. The instrumentation of public actions in relation to environmental governance derives from the states. However, if analyzed with a beyond-the-States view, governance can be understood as a process involving the participation of governmental and non-governmental actors reaching decisions, for mutual benefits, through negotiation processes. However, there is not, still, within the countries of the region, inclusive and participative governance oriented toward the sustainable use of natural resources. Although there are many challenges, in this chapter we discuss two of them: (1) to build an analytical framework to understand the environmental governance modes currently available in Latin America and (2) to generate a new sociopolitical interdisciplinary framework involving both natural and sociopolitical systems as a contribution to a new analytical framework for environmental governance. In other words, new environmental governance for Latin America.
    Interactive Coconut Tree Annotation Using Feature Space Projections
    Vargas-Munoz, John E. ; Zhou, Ping ; Falcao, Alexandre X. ; Tuia, Devis - \ 2019
    In: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. - IEEE - ISBN 9781538691557 - p. 5718 - 5721.
    The detection and counting of coconut trees in aerial images are important tasks for environment monitoring and post-disaster assessment. Recent deep-learning-based methods can attain accurate results, but they require a reasonably high number of annotated training samples. In order to obtain such large training sets with considerably reduced human effort, we present a semi-automatic sample annotation method based on the 2D t-SNE projection of the sample feature space. The proposed approach can facilitate the construction of effective training sets more efficiently than using the traditional manual annotation, as shown in our experimental results with VHR images from the Kingdom of Tonga.
    Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution
    Srivastava, Shivangi ; Vargas-Muñoz, John E. ; Tuia, Devis - \ 2019
    Remote Sensing of Environment 228 (2019). - ISSN 0034-4257 - p. 129 - 143.
    Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive. Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities). We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area of Île-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France. Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it suitable for automatic landuse map updates. Additionally, our approach could be easily scaled to multiple cities, because it is based on data sources available for many cities worldwide.
    Correcting rural building annotations in OpenStreetMap using convolutional neural networks
    Vargas-Muñoz, John E. ; Lobry, Sylvain ; Falcão, Alexandre X. ; Tuia, Devis - \ 2019
    ISPRS Journal of Photogrammetry and Remote Sensing 147 (2019). - ISSN 0924-2716 - p. 283 - 293.
    Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap (OSM), but their quality and quantity are not sufficient for training models that can create accurate rural building maps. The problems with these annotations essentially fall into three categories: (i) most commonly, many annotations are geometrically misaligned with the updated imagery; (ii) some annotations do not correspond to buildings in the images (they are misannotations or the buildings have been destroyed); and (iii) some annotations are missing for buildings in the images (the buildings were never annotated or were built between subsequent image acquisitions). First, we propose a method based on Markov Random Field (MRF) to align the buildings with their annotations. The method maximizes the correlation between annotations and a building probability map while enforcing that nearby buildings have similar alignment vectors. Second, the annotations with no evidence in the building probability map are removed. Third, we present a method to detect non-annotated buildings with predefined shapes and add their annotation. The proposed methodology shows considerable improvement in accuracy of the OSM annotations for two regions of Tanzania and Zimbabwe, being more accurate than state-of-the-art baselines.
    Multi-label building functions classification from ground pictures using convolutional neural networks
    Srivastava, S. ; Vargas Muñoz, John E. ; Swinkels, David ; Tuia, D. - \ 2018
    In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. - New York : ACM - ISBN 9781450360364 - p. 43 - 46.
    We approach the problem of multi building function classification for buildings from the city of Amsterdam using a collection of Google Street View (GSV) pictures acquired at multiple zoom levels (field of views, FoV) and the corresponding governmental census data per building. Since buildings can have multiple usages, we cast the problem as multilabel classification task. To do so, we trained a CNN model end-to-end with the task of predicting multiple co-occurring building function classes per building. We fuse the individual features of three FoVs by using volumetric stacking. Our proposed model outperforms baseline CNN models that use either single or multiple FoVs.
    Correcting Misaligned Rural Building Annotations in Open Street Map Using Convolutional Neural Networks Evidence
    Vargas-Munoz, John E. ; Marcos, Diego ; Lobry, Sylvain ; Santos, Jefersson A. dos; Falcao, Alexandre X. ; Tuia, Devis - \ 2018
    In: 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings. - IEEE Xplore - ISBN 9781538671511 - p. 1284 - 1287.
    Mapping rural buildings in developing countries is crucial to monitor and plan in those vulnerable areas. Despite the existence of some rural building annotations in OpenStreetMap (OSM), those are of insufficient quantity and quality to train models able to map large areas accurately. In particular, these annotations are very often misaligned with respect to the buildings that are present in updated aerial imagery. We propose a Markov Random Field (MRF) method to correct misaligned rural building annotations. To do so, our method uses i) the correlation between candidate aligned OSM annotations and buildings roughly detected on aerial images and ii) the local consistency of the alignment vectors.
    Land-use characterisation using Google Street View pictures and OpenStreetMap
    Srivastava, S. ; Lobry, Sylvain ; Tuia, D. ; Vargas Munoz, John - \ 2018
    - 5 p.
    This paper presents a study on the use of freely available, geo-referenced pictures from Google Street View to model and predict land-use at the urban-objects scale. This task is traditionally done manually and via photointerpretation, which is very time consuming. We propose to use a machine learning approach based on deep learning and to model land-use directly from both the pictures available from Google Street View and OpenStreetMap annotations. Because of the large availability of these two data sources, the proposed approach is scalable to cities around the globe and presents the possibility of frequent updates of the map. As base information, we use features extracted from single pictures around the object of interest; these features are issued from pre-trained convolutional neural networks. Then, we train various classifiers (Linear and RBF support vector machines, multi layer perceptron) and compare their performances. We report on a study over the city of Paris,France, where we observed that pictures coming from both inside and outside the urban-objects capture distinct, but complementary features.
    Deep learning based methods for building segmentation from remote sensing data
    Lobry, Sylvain ; Marcos Gonzalez, D. ; Vargas Munoz, John ; Kellenberger, B.A. ; Srivastava, S. ; Tuia, D. - \ 2018
    - 4 p.
    Post classification smoothing in sub-decimeter resolution images with semi-supervised label propagation
    Vargas-Munoz, John E. ; Tuia, Devis ; Santos, Jefersson A. Dos; Falcao, Alexandre X. - \ 2017
    In: 2017 IEEE International Geoscience and Remote Sensing Symposium. - Fort Worth : Institute of Electrical and Electronics Engineers Inc. (International Geoscience and Remote Sensing Symposium (IGARSS) ) - ISBN 9781509049523 - p. 3688 - 3691.
    Contextual classification - Image foresting transform - Markov random fields - Superpixel

    In this paper, we propose a post classification smoothing method aimed at improving the accuracy and visual appearance of sub-decimeter image classification results. Starting from the class confidence maps of a supervised classifier, we find a set of high confidence markers and propagate labels on an extended region adjacency graph. We apply the proposed method on a challenging 5cm resolution dataset over Potsdam, Germany. The proposed algorithm outperforms state-of-the-art post classification smoothing algorithms both when the classifier is trained specifically on the image and when it is trained and tested in different set of images.

    Induction of sperm activation in open and closed thelycum penaeoid shrimps
    Alfaro Montoya, J. ; Munoz, N. ; Vargas, M. ; Komen, J. - \ 2003
    Aquaculture 216 (2003). - ISSN 0044-8486 - p. 371 - 382.
    sicyonia-ingentis - marine shrimp - setiferus - hybridization - decapoda
    A modified egg water (EW) technique for in vitro induction of sperm activation was applied to Trachypenaeus byrdi, Xiphopenaeus riveti (closed thelycum shrimps), and Litopenaeus occidentalis (open thelycum) from a tropical estuary, Golfo de Nicoya, Costa Rica. The study was designed to investigate the changes that occur in the sperm following contact with egg water, and to determine the potential of the technique for the assessment of differences in quality between sperm from spermatophores and sperm taken from the seminal receptacles. The modified technique induced activation of sperm removed from females' seminal receptacles, and demonstrated that sperm from males of T byrdi and X riveti do not react against conspecific EW, indicating that further maturation is required in seminal receptacles. Sperm from wild males of L. occidentalis reacted against conspecific EW, but at a low rate, suggesting that further maturation may be required in the external surface of the thelycum. Activation rates were low or variable between individuals in each species despite the expected high sperm quality from wild shrimp, indicating that the technique is not yet an useful sperm quality assay for the captive reproduction industry. The interspecific interaction between T byrdi sperm (seminal receptacles) and EW from X riveti and L. occidentalis generated no acrosome reaction, which may be an indication that molecular recognition is missing. (C) 2003 Elsevier Science B.V. All rights reserved.
    Check title to add to marked list

    Show 20 50 100 records per page

    Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.