Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows

Tiede, Dirk; Krafft, Pascal; Füreder, Petra; Lang, Stefan

doi:10.3390/rs9040326

Cited by 23 publications

(23 citation statements)

References 28 publications

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“…OBIA-methods represent the state-of-the art in remote sensing for object detection [31], high-resolution land-cover mapping [32,33] and change detection [34]. However, contrarly to CNNs, OBIA-based models are not learnable models, i.e., OBIA can not directly re-utilize the learning from one image into another.…”

Section: Obia-based Detectionmentioning

confidence: 99%

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

et al. 2017

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There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).

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Section: Obia-based Detectionmentioning

confidence: 99%

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

et al. 2017

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“…Users of this toolkit can improve its accuracy by applying semi-automatic image analysis methods, as demonstrated by [8,28,30]. Semi-automatic image analysis combines automatic image analysis, as demonstrated in this study, with manual edits [8].…”

Section: Future Workmentioning

confidence: 91%

“…This is highlighted by the significant increase in margin of error and RMSE for test camps not used to develop the definition files despite being in close proximity with the camps used to develop the definition file. Additionally, seasonal variations can affect spectral signatures [30]. Lastly, tree and cloud cover can impede automated extraction efforts, thus leading to misclassification or the inability to conduct any extraction [17].…”

Section: Limitations Of Imagery-derived Estimatesmentioning

confidence: 99%

Refugee Camp Population Estimates Using Automated Feature Extraction

Green¹,

Blanford²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Throughout 2018, approximately 68.5 million people were forcibly displaced due to armed conflict, generalized violence, or human rights violations around the world; of those, 40 million were internally displaced persons (IDP), 25.4 million refugees, and 3.1 million asylum-seekers. Effective management of refugee and IDP camps rely on accurate, up-to-date, and comprehensive population estimates. However, obtaining this information is not always easy. Thus, the purpose of this study was to develop a methodology and custom toolset that estimates populations based on dwellings derived from automated feature extraction of high-resolution, multi-spectral orthorectified imagery. Estimates were determined for five Rohingya refugee camp populations and compared with United Nations High Commissioner for Human Rights (UNHCR) baseline data to determine accuracy.

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“…the process of finding a pattern in an image, is widely used in remote sensing to address a variety of problems including road extraction (Hu, Zhang, & Tao, 2004) or dwelling detection. The integration of stratified template matching methods in an OBIA workflow has shown to improve the accuracy of dwelling extraction (Laneve et al, 2006;Tiede, Krafft, Füreder, & Lang, 2017).…”

Section: Information Extraction and Analysismentioning

confidence: 99%

Earth observation tools and services to increase the effectiveness of humanitarian assistance

Lang

Füreder

Riedler

et al. 2019

European Journal of Remote Sensing

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Humanitarian action has rapidly adopted Earth observation (EO) and geospatial technologies shaping them according to their needs. Protracted crises and large-scale population displacements require up-to-date information in many facets of humanitarian action support, from mission planning, resource deployment and monitoring, to nutrition and vaccination campaigns, camp plotting, damage assessment, etc. Even though nearly all assets of remote sensing apply in such demanding scenarios, it remains a challenge to fully implement and sustain a trustful and reliable information service. This paper discusses achievements and open issues in the use and uptake of EO technology, from a technical and organisational point of view, motivated by an information service for Médecins Sans Frontières (MSF) and its extension to other NGO's information needs in the humanitarian sector. With a focus on EO-based population estimation based on (semi-)automated dwelling counting from very high-resolution optical satellite imagery as well as the exploitation of data integration (including radar sensors), the paper also covers potential service elements with respect to environmental and ground-or surface water monitoring. It investigates workflow elements in relation to information extraction and delivery by illustrating a broad range of application scenarios, and discusses first operational solutions of a customized service portfolio.

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Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows

Cited by 23 publications

References 28 publications

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Refugee Camp Population Estimates Using Automated Feature Extraction

Earth observation tools and services to increase the effectiveness of humanitarian assistance

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