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A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, Vol 174 (April 2021)
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Titre : A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; Diogo Nunes Gonçalves, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] carte agricole
[Termes descripteurs IGN] Citrus sinensis
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] comptage
[Termes descripteurs IGN] cultures
[Termes descripteurs IGN] détection d'objet
[Termes descripteurs IGN] extraction de la végétation
[Termes descripteurs IGN] gestion durable
[Termes descripteurs IGN] image captée par drone
[Termes descripteurs IGN] maïs (céréale)
[Termes descripteurs IGN] rendement agricoleRésumé : (auteur) Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering croplands nowadays. However, visual inspection of such images can be a challenging and biased task, specifically for detecting plants and rows on a one-step basis. Thus, developing an architecture capable of simultaneously extracting plant individually and plantation-rows from UAV-images is yet an important demand to support the management of agricultural systems. In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in (a) a cornfield (Zea mays L.) with different growth stages (i.e. recently planted and mature plants) and in a (b) Citrus orchard (Citrus Sinensis Pera). Both datasets characterize different plant density scenarios, in different locations, with different types of crops, and from different sensors and dates. This scheme was used to prove the robustness of the proposed approach, allowing a broader discussion of the method. A two-branch architecture was implemented in our CNN method, where the information obtained within the plantation-row is updated into the plant detection branch and retro-feed to the row branch; which are then refined by a Multi-Stage Refinement method. In the corn plantation datasets (with both growth phases – young and mature), our approach returned a mean absolute error (MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038, precision and recall values of 0.856, and 0.905, respectively, and an F-measure equal to 0.876. These results were superior to the results from other deep networks (HRNet, Faster R-CNN, and RetinaNet) evaluated with the same task and dataset. For the plantation-row detection, our approach returned precision, recall, and F-measure scores of 0.913, 0.941, and 0.925, respectively. To test the robustness of our model with a different type of agriculture, we performed the same task in the citrus orchard dataset. It returned an MAE equal to 1.409 citrus-trees per patch, MRE of 0.0615, precision of 0.922, recall of 0.911, and F-measure of 0.965. For the citrus plantation-row detection, our approach resulted in precision, recall, and F-measure scores equal to 0.965, 0.970, and 0.964, respectively. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops. The method proposed here may be applied to future decision-making models and could contribute to the sustainable management of agricultural systems. Numéro de notice : A2021-205 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.024 date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97171
in ISPRS Journal of photogrammetry and remote sensing > Vol 174 (April 2021) . - pp 1 - 17[article]Development and assessment of rainwater harvesting suitability map using analytical hierarchy process, GIS and RS techniques / Khaled S. Balkhair in Geocarto international, vol 36 n° 4 ([15/03/2021])
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Titre : Development and assessment of rainwater harvesting suitability map using analytical hierarchy process, GIS and RS techniques Type de document : Article/Communication Auteurs : Khaled S. Balkhair, Auteur ; Khalil Ur Rahman, Auteur Année de publication : 2021 Article en page(s) : pp 421 - 448 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes descripteurs IGN] aide à la décision
[Termes descripteurs IGN] analyse de sensibilité
[Termes descripteurs IGN] Arabie Saoudite
[Termes descripteurs IGN] bassin hydrographique
[Termes descripteurs IGN] carte hydrographique
[Termes descripteurs IGN] eau pluviale
[Termes descripteurs IGN] écoulement des eaux
[Termes descripteurs IGN] gestion de l'eau
[Termes descripteurs IGN] processus d'analyse hiérarchique
[Termes descripteurs IGN] système d'information géographiqueRésumé : (auteur) Rainwater harvesting (RWH), which is the collection and storage of rainwater for multiple purposes, is gaining recognition in water supply issues. Selection of harvesting sites is the most critical factor in RWH projects. The objective of this study is to develop a suitability map of RWH sites for a basin in Saudi Arabia. The method used, constitute the identification and assigning weights to criteria, and generation of suitability map using Analytical Hierarchy Process (AHP). Eight appropriate criteria were considered. Results showed that excellent and good sites covered about 40.6% of the total available sites. Sensitivity analysis showed that the curve number (CN), slope, rainfall and soil were the most influential criteria. The maximum increase in the percentage area of excellent sites was 92% while good and moderate classes decreased by 43 and 53%, respectively. The developed suitability maps provide useful information to the decision maker for use in water management. Numéro de notice : A2021-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.160859 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1608591 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97082
in Geocarto international > vol 36 n° 4 [15/03/2021] . - pp 421 - 448[article]Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([15/03/2021])
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Titre : Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR Type de document : Article/Communication Auteurs : Kabir Peerbhay, Auteur ; Onisimo Mutanga, Auteur ; Romano Lottering, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 465 - 480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] carte de la végétation
[Termes descripteurs IGN] classification non dirigée
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] espèce exotique envahissante
[Termes descripteurs IGN] forêt ripicole
[Termes descripteurs IGN] image hyperspectrale
[Termes descripteurs IGN] précision cartographique
[Termes descripteurs IGN] semis de pointsRésumé : (auteur) Accurate spatial information on the location of invasive alien plants (IAPs) in riparian environments is critical to fulfilling a comprehensive weed management regime. This study aimed to automatically map the occurrence of riparian bugweed (Solanum mauritianum) using airborne AISA Eagle hyperspectral data (393 nm–994 nm) in conjunction with LiDAR derived height. Utilising an unsupervised random forest (RF) classification approach and Anselin local Moran’s I clustering, results indicate that the integration of LiDAR with minimum noise fraction (MNF) produce the best detection rate (DR) of 88%, the lowest false positive rate (FPR) of 7.14% and an overall mapping accuracy of 83% for riparian bugweed. In comparison, utilising the original hyperspectral wavebands with and without LiDAR produced lower DRs and higher FPRs with overall accuracies of 79% and 68% respectively. This research demonstrates the potential of combining spectral information with LiDAR to accurately map IAPs using an automated unsupervised RF anomaly detection framework. Numéro de notice : A2021-163 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1614101 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1614101 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97084
in Geocarto international > vol 36 n° 4 [15/03/2021] . - pp 465 - 480[article]Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification / Fei Chen in Geocarto international, vol 36 n° 3 ([01/03/2021])
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Titre : Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification Type de document : Article/Communication Auteurs : Fei Chen, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 262 - 280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] analyse spatio-temporelle
[Termes descripteurs IGN] carte d'utilisation du sol
[Termes descripteurs IGN] Chine
[Termes descripteurs IGN] classification et arbre de régression
[Termes descripteurs IGN] désertification
[Termes descripteurs IGN] données spatiotemporelles
[Termes descripteurs IGN] image Landsat-OLI
[Termes descripteurs IGN] image Landsat-TM
[Termes descripteurs IGN] karst
[Termes descripteurs IGN] lithologieRésumé : (auteur) Karst Rocky Desertification (KRD) has become the most serious ecological disaster in Southwest China. We used the data of Thematic Mapper (TM) images from 1990, 1995, 2000, 2004, and 2011 and the 2016 Operational Land Imager (OLI) image. These sensing images were pre-processed by removing non-karst areas based on lithology and cutting away the land types impossibly generating KRD from land use maps. Then, we used a Classification And Regression Tree (CART) to classify the KRD. We want to improve the interpretation accuracy of KRD through the above steps. The results were as follows: (1) The KRD experiences the evolution process of ‘first deterioration and then improvement’. The rate is −4.94 km2.a−1 over a period of 1990 to 2004, and the rate is 36.48 km2.a−1 from 2004 to 2016; (2) The most influential factors causing KRD formation are the lithology and the resident population density, with contribution rates of 30.17% and 25.86%, respectively. Numéro de notice : A2021-140 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595175 date de publication en ligne : 18/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595175 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97036
in Geocarto international > vol 36 n° 3 [01/03/2021] . - pp 262 - 280[article]Famous charts and forgotten fragments: exploring correlations in early Portuguese nautical cartography / Bruno Almeida in International journal of cartography, vol 7 n° 1 (March 2021)
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Titre : Famous charts and forgotten fragments: exploring correlations in early Portuguese nautical cartography Type de document : Article/Communication Auteurs : Bruno Almeida, Auteur Année de publication : 2021 Article en page(s) : pp 38 - 59 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie ancienne
[Termes descripteurs IGN] analyse comparative
[Termes descripteurs IGN] cartographie étrangère
[Termes descripteurs IGN] cartométrie
[Termes descripteurs IGN] corrélation automatique de points homologues
[Termes descripteurs IGN] histoire de la cartographie
[Termes descripteurs IGN] Portugal
[Termes descripteurs IGN] portulan
[Termes descripteurs IGN] seizième siècle
[Termes descripteurs IGN] toponymie localeRésumé : (Auteur) The authors of the well-known collection Portugaliae Monumenta Cartographica hinted at connections between two anonymous portolan charts from the beginning of the sixteenth century, namely the portolan chart at the Bibliothèque Municipale of Dijon and a fragment of a chart kept in Lisbon in the Archive at Torre do Tombo. Later, they also mentioned affinities between those two charts and the famous chart known as Kunstmann III. However, they did not pursue these observations further. The present paper proceeds from where those researchers stopped investigating and proposes a fresh look on this cartographic material by combining a traditional historical approach with modern digital techniques. First, a comparative study of the toponomy of a common area of the charts will be presented. Later, each chart will be examined with the help of cartometric methods to access their implicit geometry. The advancements on the study of correlations between these charts will be shown, thus confirming that the combination of traditional and digital methods of investigation open very promising perspectives to the study of unsolved questions in the History of Cartography. Numéro de notice : A2021-182 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1705226 date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.1080/23729333.2019.1705226 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97101
in International journal of cartography > vol 7 n° 1 (March 2021) . - pp 38 - 59[article]Integrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques / Hamid Karimi in Geocarto international, vol 36 n° 3 ([01/03/2021])
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PermalinkIdentifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
PermalinkLand cover harmonization using Latent Dirichlet Allocation / Zhan Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
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