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Deep Learning for Classification Tasks on Geospatial Vector Polygons [article]

Rein van 't Veer and Peter Bloem and Erwin Folmer
2019 arXiv   pre-print
In this paper, we evaluate the accuracy of deep learning approaches on geospatial vector geometry classification tasks.  ...  Instead, our introduced deep neural net architectures are able to learn on sequences of coordinates mapped directly from polygons.  ...  The data for the buildings task was published by the Dutch National Cadastre under a CC Zero license.  ... 
arXiv:1806.03857v2 fatcat:xsbykchvtjczfnadhcmywiewnq

TIML: Task-Informed Meta-Learning for Agriculture [article]

Gabriel Tseng and Hannah Kerner and David Rolnick
2022 arXiv   pre-print
While we focus on tasks from agriculture, TIML could offer benefits to any meta-learning setup with task-specific metadata, such as classification of geo-tagged images and species distribution modelling  ...  We build on previous work exploring the use of meta-learning for agricultural contexts in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning  ...  learning models directly Classification Task.  ... 
arXiv:2202.02124v1 fatcat:3mtitlt6srcehiretruhug6fwu

Deep Learning Techniques for Geospatial Data Analysis [chapter]

Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam
2020 Learning and Analytics in Intelligent Systems  
(ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques  ...  The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data.  ...  The work presented in this chapter is based on the course material developed to train engineering teachers on the topics of Geospatial Analysis and Product Design Engineering.  ... 
doi:10.1007/978-3-030-49724-8_3 fatcat:yv6stldjcjbclbfcx3d6i3s2um


R. Can, S. Kocaman, A. O. Ok
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
By integrating the novel artificial intelligence (AI) methods including deep learning (DL) algorithms on WebGIS interfaces, this task can be achieved.  ...  In this study, a web-based geospatial AI (GeoAI) platform was developed for map updating by using the image processing results obtained from a DL algorithm to assist volunteers.  ...  Deep learning (DL) architectures, especially deep convolutional neural networks (CNNs), have increasingly been used for semantic segmentation/classification of airborne imagery (e.g.  ... 
doi:10.5194/isprs-archives-xliii-b5-2021-13-2021 fatcat:a6pvrl4b65cq5czhqgfvlhkttq

End-to-End Learning of Polygons for Remote Sensing Image Classification

Nicolas Girard, Yuliya Tarabalka
2018 IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium  
We here cast a mapping problem as a polygon prediction task, and propose a deep learning approach which predicts vertices of the polygons outlining objects of interest.  ...  This paper studies if one can learn to directly output a vectorial semantic labeling of the image.  ...  We formulate a mapping problem as a polygon prediction task.  ... 
doi:10.1109/igarss.2018.8518116 dblp:conf/igarss/GirardT18 fatcat:i4l6mgz3hfcr7csfuonmxk7rgq

A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery

Ziming Li, Qinchuan Xin, Ying Sun, Mengying Cao
2021 Remote Sensing  
for different tasks.  ...  In this study, we proposed a novel deep learning-based framework for automated extraction of building footprint polygons (DLEBFP) from very high-resolution aerial imagery by combining deep learning models  ...  We are also grateful to ISPRS for providing the Vaihingen dataset. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13183630 fatcat:vnuxfbpbkvalbic6k6yusijzqm

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning [article]

Conrad M Albrecht, Fernando Marianno, Levente J Klein
2022 arXiv   pre-print
The general method proposed here is platform independent, and it can be adapted to generate labels for other satellite modalities in order to enable machine learning on overhead imagery for land use classification  ...  A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data.  ...  Random Forest [22] , Support Vector Machine [23] , XGBoost [24] , and a plethora of deep learning models [25] .  ... 
arXiv:2202.00067v1 fatcat:ytf3422ggbfctbeqaxgz3gby3e


M. Ivić
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
</strong> For quick and efficient response, as well as for recovery after any natural or artificial catastrophe, one of the most important things are accurate and reliable spatial data in real or near  ...  This paper presents an overview of the use of AI in geospatial analysis in disaster management.</p>  ...  Fifteen training samples (polygons) for each class were randomly created on a pixel-by-pixel basis.  ... 
doi:10.5194/isprs-archives-xlii-3-w8-161-2019 fatcat:uhqreow4qvc6fg7iamccy4q6y4


A. Gujrathi, C. Yang, F. Rottensteiner, K. M. Buddhiraju, C. Heipke
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
on small polygons.  ...  For this challenge, we adopt the method of Yang et al. (2019) to decompose polygons into regular patches of fixed size.  ...  providing the test data and for their support of this project.  ... 
doi:10.5194/isprs-archives-xliii-b2-2020-667-2020 fatcat:wjbavp4mxbd2tb4ukoahcpsytu

A Review of Location Encoding for GeoAI: Methods and Applications [article]

Gengchen Mai, Krzysztof Janowicz, Yingjie Hu, Song Gao, Bo Yan, Rui Zhu, Ling Cai, Ni Lao
2021 arXiv   pre-print
One fundamental step is to encode a single point location into an embedding space, such that this embedding is learning-friendly for downstream machine learning models such as support vector machines and  ...  ), polygons (e.g., administrative regions), graphs (e.g., transportation networks), or rasters (e.g., remote sensing images), in a hidden embedding space so that they can be readily incorporated into deep  ...  Fei Du for his comments on the differences between location encoding and geohash. We also want to Thank Prof. Stefano Ermon for his suggestions on unsupervised learning for location encoding.  ... 
arXiv:2111.04006v1 fatcat:ymv527cygbaavbrhi2xe5xtcni

Large-Scale Classification of Urban Structural Units from Remote Sensing Imagery

Jacob Arndt, Dalton Lunga
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines.  ...  Index Terms-Deep learning, image classification, remote sensing, settlements, urban, urban structural units (USUs), urban structure types (USTs).  ...  The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines.  ... 
doi:10.1109/jstars.2021.3052961 fatcat:jnw4umtc5zbhfigoewj5pbnare

Mask R-CNN for Geospatial Object Detection

Dalal AL-Alimi, Faculty of Engineering, Sana'a University, Sana'a, Yemen, Yuxiang Shao, Ahamed Alalimi, Ahmed Abdu
2020 International Journal of Information Technology and Computer Science  
Geospatial imaging technique has opened a door for researchers to implement multiple beneficial applications in many fields, including military investigation, disaster relief, and urban traffic control  ...  Mask R-CNN had been designed to identify an object outlines at the pixel level (instance segmentation), and for object detection in natural images.  ...  Light blue is the objects annotated by polygon points and the other by bounding boxes [23] , for each class in both dataset. Fig. 7 . 7 Fig.7. Loss values of each task in Mask R-CNN.  ... 
doi:10.5815/ijitcs.2020.05.05 fatcat:mkuj6gfxq5fyvigxmhwuuiur7a

OpenStreetMap data quality assessment via deep learning and remote sensing imagery

Xuejing Xie, Yi Zhou, Yongyang Xu, Yunbing Hu, Chunling Wu
2019 IEEE Access  
This work focuses on how to assess the quality of OSM via deep learning and high-resolution remote imagery.  ...  First, considering that high-resolution remote sensing imagery is clear enough for recognizing buildings, we proposed using multi-task deep-convolutional networks to extract buildings in pixel level.  ...  Adam is one of the most widely used algorithms [41] in deep learning.  ... 
doi:10.1109/access.2019.2957825 fatcat:7stkuxiq7jgvhfzrtnlr25riy4


C. Witharana, M. A. E. Bhuiyan, A. K. Liljedahl
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The MAPLE uses deep learning (DL) convolutional neural nets (CNNs) algorithms on HPCs to automatically map IWPs from VHSR commercial satellite imagery across large geographic domains.  ...  We trained and tasked a DLCNN semantic object instance segmentation algorithm to automatically classify IWPs from VHSR satellite imagery.  ...  Authors would like to thank Polar Geospatial Center, University of Minnesota for imagery support.  ... 
doi:10.5194/isprs-archives-xliv-m-2-2020-111-2020 fatcat:ydrrnrfkdbcydaenhdfuysagna


S. Saupi Teri, I. A. Musliman, A. Abdul Rahman
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Some geospatial applications continue to rely on conventional geospatial processing, where limitation on computation capabilities often lacking to attain significant data interpretation.  ...  This paper also addresses the GPU future trends advancement opportunities with other technologies, machine learning, deep learning, and cloud-based computing.  ...  At the end of the paper, a remarkable view on future trends of the GPU utilisation in other platform such as machine learning, deep learning, TPU, AI, cloud computing and the beneficial use in geospatial  ... 
doi:10.5194/isprs-archives-xlvi-4-w3-2021-295-2022 fatcat:jag5ozvybfaprdtgoykqmyutea
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