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Evaluating the impact of declining tsetse fly (Glossina pallidipes) habitat in the Zambezi valley of Zimbabwe / Farai Matawa in Geocarto international, vol 35 n° 12 ([01/09/2020])  

Public [article]  inGeocarto international > vol 35 n° 12 [01/09/2020] . - pp 1373 - 1384 Titre : Evaluating the impact of declining tsetse fly (Glossina pallidipes) habitat in the Zambezi valley of Zimbabwe Type de document : Article/Communication Auteurs : Farai Matawa, Auteur ; Amon Murwira, Auteur ; Peter M. Atkinson, Auteur Année de publication : 2020 Article en page(s) : pp 1373 - 1384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] bovin [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] diptère [Termes descripteurs IGN] distance [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] habitat d'espèce [Termes descripteurs IGN] maladie parasitaire [Termes descripteurs IGN] Zambèze (fleuve) [Termes descripteurs IGN] Zimbabwe Résumé : (auteur) Tsetse flies transmit trypanosomes that cause Human African Trypanosomiasis (HAT) in humans and African Animal Trypanosomiasis (AAT) in animals. Understanding historical trends in the spatial distribution of tsetse fly habitat is necessary for planning vector control measures. The objectives of this study were (i) to test for evidence of any trends in suitable tsetse fly habitat and (ii) to test whether there is an association between trypanosomiasis detected from livestock sampled in dip tanks and local tsetse habitat in the project area. Results indicate a significant decreasing trend in the amount of suitable habitat. There is no significant correlation between trypanosomiasis prevalence rates in cattle and distance from patches of suitable tsetse habitat. The observed low trypanosomiasis prevalence and the lack of dependence on suitable tsetse fly habitat can be explained by the observed decreases in suitable tsetse habitat, which themselves are due to expansion of settlement and agriculture in North Western Zimbabwe. Numéro de notice : A2020-486 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1576780 date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1576780 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95653 [article]

A lightweight ensemble spatiotemporal interpolation model for geospatial data / Shifen Cheng in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1849 - 1872 Titre : A lightweight ensemble spatiotemporal interpolation model for geospatial data Type de document : Article/Communication Auteurs : Shifen Cheng, Auteur ; Peng Peng, Auteur ; Feng Lu, Auteur Année de publication : 2020 Article en page(s) : pp 1849 - 1872 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] distance pondérée [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] erreur absolue [Termes descripteurs IGN] interpolation spatiale [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] requête spatiotemporelle Résumé : (auteur) Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations. Numéro de notice : A2020-484 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1725016 date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1725016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95651 [article]

Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine / Aparna R. Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 104 - 122 Titre : Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine Type de document : Article/Communication Auteurs : Aparna R. Phalke, Auteur ; Mutlu Ozdogan, Auteur ; Prasad S. Thenkabail, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 104 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Asie centrale [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Corine Land Cover [Termes descripteurs IGN] Europe (géographie politique) [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] photo-interprétation [Termes descripteurs IGN] Russie [Termes descripteurs IGN] surface cultivée [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area – defined as continental to global – cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 countries covering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) platform. We used a pixel-based Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 93.8% with roughly 14% omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of net croplands (nearly 30% of global net cropland areas) occupying 18% of the study land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. This Landsat-derived 30-m cropland product (GFSAD30) provided 10–30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30 m global land cover (GLC30) product providing an overall similarity of 88.8% (Kappa 0.7) with producer’s cropland similarity of 89.2% (errors of omissions = 10.8%) and user’s cropland similarity of 81.8% (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strengths of GFSAD30 product, compared to other products, were: 1. identifying precise location of croplands, and 2. capturing fragmented croplands. The cropland extent map dataset is available through NASA’s Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global Croplands website, GEE code is accessible at: https://code.earthengine.google.com/1666e8bed34e0ce2b2aaf1235ad8c6bd. Numéro de notice : A2020-547 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.022 date de publication en ligne : 18/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95773 [article]

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Mining regional patterns of land use with adaptive adjacent criteria / Xinmeng Tu in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 418 - 431 Titre : Mining regional patterns of land use with adaptive adjacent criteria Type de document : Article/Communication Auteurs : Xinmeng Tu, Auteur ; Zhenjie Chen, Auteur ; Beibei Wang, Auteur ; changqing Xu, Auteur Année de publication : 2020 Article en page(s) : pp 418 - 431 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] adjacence [Termes descripteurs IGN] analyse combinatoire (maths) [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] Chine [Termes descripteurs IGN] construction [Termes descripteurs IGN] extraction de modèle [Termes descripteurs IGN] filtrage spatiotemporel [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] polygone [Termes descripteurs IGN] région [Termes descripteurs IGN] relation spatiale [Termes descripteurs IGN] surface cultivée [Termes descripteurs IGN] urbanisation [Termes descripteurs IGN] utilisation du sol [Termes descripteurs IGN] variogramme Résumé : (auteur) Land use/cover changes (LULC) are complicated and regionally diverse. When mining regional patterns, the use of a spatial relationship that is determined without considering the spatial correlation among geographical objects can lead to problematic results, e.g. mistakenly treating unrelated objects as adjacent. Additionally, traditional prevalence measures are unstable for uneven datasets such as LULC, wherein some land-use change types show small numbers and uneven quantities, and valuable rules for some land-use categories may be ignored. Therefore, we proposed a regional pattern mining method. First, we developed adaptive adjacent criteria, which can be automatically generated for each specific zone to define adjacency for better spatial-temporal mining. Then, a combinational decision model was built to improve the stability of the prevalence measure, which was used to filter out the insignificant spatial-temporal rules. Furthermore, we proposed two levels of land-use pattern mining, i.e. cluster-level mining and polygon-level mining, to first discover hot-spot areas where similar land-use change has occurred frequently and then to determine the location, frequency, and change time of rules related to different land-use activities. The proposed method was used for mining the dependence of land use and regional patterns on land-use changes. Results show that the proposed method can determine the spatial dependence between the land-use categories, as well as regional patterns of land-use changes. According to our research, the study area, Xinbei District, China, is undergoing land-use change involving rapid urbanization, extensive transportation construction, and losses of farmland. Numéro de notice : A2020-487 Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1761452 date de publication en ligne : 18/06/2020 En ligne : https://doi.org/10.1080/15230406.2020.1761452 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95655 [article]

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OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier / Quy Thy Truong in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - n° 504 Titre : OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier Type de document : Article/Communication Auteurs : Quy Thy Truong , Auteur ; Guillaume Touya , Auteur ; Cyril de Runz, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : n° 504 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] qualité des données Résumé : (auteur) Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”. Numéro de notice : A2020-507 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090504 date de publication en ligne : 22/08/2020 En ligne : https://doi.org/10.3390/ijgi9090504 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95682 [article]

Spatial simulation of rainstorm waterlogging based on a water accumulation diffusion algorithm / Jingwei Hou in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)  

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Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience / Yingwei Yan in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

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Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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CNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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A regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-4 (August 2020)  

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SemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-5 (August 2020)  

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A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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Comparative study of different models for soil erosion and sediment yield in Pairi watershed, Chhattisgarh, India / Tarun Kumar in Geocarto international, vol 35 n° 11 ([01/08/2020])  

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Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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