Nölke, Nils

[["dc.bibliographiccitation.journal","Neural Computing and Applications"],["dc.contributor.author","Freudenberg, Maximilian"],["dc.contributor.author","Magdon, Paul"],["dc.contributor.author","Nölke, Nils"],["dc.date.accessioned","2022-09-01T09:49:17Z"],["dc.date.available","2022-09-01T09:49:17Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract\n \n We present a deep learning-based framework for individual tree crown delineation in aerial and satellite images. This is an important task, e.g., for forest yield or carbon stock estimation. In contrast to earlier work, the presented method creates irregular polygons instead of bounding boxes and also provides a tree cover mask for areas that are not separable. Furthermore, it is trainable with low amounts of training data and does not need 3D height information from, e.g., laser sensors. We tested the approach in two scenarios: (1) with 30 cm WorldView-3 satellite imagery from an urban region in Bengaluru, India, and (2) with 5 cm aerial imagery of a densely forested area near Gartow, Germany. The intersection over union between the reference and predicted tree cover mask is 71.2% for the satellite imagery and 81.9% for the aerial images. On the polygon level, the method reaches an accuracy of 46.3% and a recall of 63.7% in the satellite images and an accuracy of 52% and recall of 66.2% in the aerial images, which is comparable to previous works that only predicted bounding boxes. Depending on the image resolution, limitations to separate individual tree crowns occur in situations where trees are hardly separable even for human image interpreters (e.g., homogeneous canopies, very small trees). The results indicate that the presented approach can efficiently delineate individual tree crowns in high-resolution optical images. Given the high availability of such imagery, the framework provides a powerful tool for tree monitoring. The source code and pretrained weights are publicly available at\n https://github.com/AWF-GAUG/TreeCrownDelineation\n ."],["dc.description.sponsorship"," Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.description.sponsorship"," Bundesministerium fur Verkehr und Digitale Infrastruktur http://dx.doi.org/10.13039/100008383"],["dc.description.sponsorship"," Georg-August-Universität Göttingen 501100003385"],["dc.identifier.doi","10.1007/s00521-022-07640-4"],["dc.identifier.pii","7640"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113385"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-597"],["dc.relation.eissn","1433-3058"],["dc.relation.issn","0941-0643"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Individual tree crown delineation in high-resolution remote sensing images based on U-Net"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","312"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Freudenberg, Maximilian"],["dc.contributor.author","Nölke, Nils"],["dc.contributor.author","Urban, Kira"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Kleinn, Christoph"],["dc.contributor.author","Agostini, Alejandro"],["dc.date.accessioned","2019-07-09T11:50:04Z"],["dc.date.available","2019-07-09T11:50:04Z"],["dc.date.issued","2019"],["dc.description.abstract","Oil and coconut palm trees are important crops in many tropical countries, which are either planted as plantations or scattered in the landscape. Monitoring in terms of counting provides useful information for various stakeholders. Most of the existing monitoring methods are based on spectral profiles or simple neural networks and either fall short in terms of accuracy or speed. We use a neural network of the U-Net type in order to detect oil and coconut palms on very high resolution satellite images. The method is applied to two different study areas: (1) large monoculture oil palm plantations in Jambi, Indonesia, and (2) coconut palms in the Bengaluru Metropolitan Region in India. The results show that the proposed method reaches a performance comparable to state of the art approaches, while being about one order of magnitude faster. We reach a maximum throughput of 235 ha/s with a spatial image resolution of 40 cm. The proposed method proves to be reliable even under difficult conditions, such as shadows or urban areas, and can easily be transferred from one region to another. The method detected palms with accuracies between 89% and 92%."],["dc.description.sponsorship","Open-Acces-Publikationsfonds 2019"],["dc.identifier.doi","10.3390/rs11030312"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15850"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59694"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.relation","SFB 990: Ökologische und sozioökonomische Funktionen tropischer Tieflandregenwald-Transformationssysteme (Sumatra, Indonesien)"],["dc.relation","SFB 990 | B | B05: Land use patterns in Jambi - quantification of structure, heterogeneity and changes of vegetation and land use as a basis for the explanation of ecological and socioeconomic functions"],["dc.relation.eissn","2072-4292"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.subject.ddc","570"],["dc.subject.gro","sfb990_journalarticles"],["dc.title","Large Scale Palm Tree Detection In High Resolution Satellite Images Using U-Net"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]