Refugee Camp Population Estimates Using Automated Feature Extraction

Green, Brandon; Blanford, Simon

doi:10.24251/hicss.2020.268

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…The diversity of spatio-temporal data on refugee settlements is beneficial for a targeted assessment of their inclusion in broad-scale human settlement products, yet there has never been a formal assessment of the detection of refugee settlements. There are, by contrast, many analyses of individual refugee settlements using high or moderate resolution (e.g., Sentinel and Landsat) satellite imagery or derived products to estimate settlement area [31,32], enumerate dwellings [33][34][35][36][37], model refugee populations [38], guide the delivery of aid and relief [39,40], assess environmental conditions [41][42][43][44], map land cover/use change [45][46][47] and quantify economic development [48].…”

Section: Introductionmentioning

confidence: 99%

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

Hoek

Friedrich

2021

Remote Sensing

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Satellite-based broad-scale (i.e., global and continental) human settlement data are essential for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology and demographic modeling. Many human settlement products report exceptional detection accuracies above 85%, but there is a substantial blind spot in that product validation typically focuses on large urban areas and excludes rural, small-scale settlements that are home to 3.4 billion people around the world. In this study, we make use of a data-rich sample of 30 refugee settlements in Uganda to assess the small-scale settlement detection by four human settlement products, namely, Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE), Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), High Resolution Settlement Layer (HRSL) and World Settlement Footprint (WSF). We measured each product’s areal coverage within refugee settlement boundaries, assessed detection of 317,416 building footprints and examined spatial agreement among products. For settlements established before 2016, products had low median probability of detection and F1-score of 0.26 and 0.24, respectively, a high median false alarm rate of 0.59 and tended to only agree in regions with the highest building density. Individually, GRID3-SE offered more than five-fold the coverage of other products, GHS-BUILT-S2 underestimated the building footprint area by a median 50% and HRSL slightly underestimated the footprint area by a median 7%, while WSF entirely overlooked 8 of the 30 study refugee settlements. The variable rates of coverage and detection partly result from GRID3-SE and HRSL being based on much higher resolution imagery, compared to GHS-BUILT-S2 and WSF. Earlier established settlements were generally better detected than recently established settlements, showing that the timing of satellite image acquisition with respect to refugee settlement establishment also influenced detection results. Nonetheless, settlements established in the 1960s and 1980s were inconsistently detected by settlement products. These findings show that human settlement products have far to go in capturing small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validating human settlement detection approaches.

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Section: Introductionmentioning

confidence: 99%

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

Hoek

Friedrich

2021

Remote Sensing

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“…The second is studies of forced displacement, such as research identifying population flows [e.g. ( 62 )] or refugee dwelling structures [e.g. ( 54 )].…”

Section: Mapping the Fieldmentioning

confidence: 99%

“…For our review, we distinguish between traditional and artificial intelligence (AI)-based detection techniques. Human experts set the rules or define the procedure for traditional techniques, such as algebra-based methods [see ( 62 ), p 2]. Note that our use of the word “traditional” does not imply outdated; indeed, some of the reviewed studies use highly sophisticated signal processing techniques, such as interferometric synthetic aperture radar (InSAR) [e.g.…”

Section: Mapping the Fieldmentioning

confidence: 99%

Toward the remote monitoring of armed conflicts

2023

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The war in Ukraine has pushed the role of satellite imagery in armed conflicts into the spotlight. For a long time, satellite images were primarily used for military and intelligence purposes, but today they permeate every aspect of armed conflicts. Their importance in influencing the course of armed conflicts will further grow as progress in deep learning makes automated analysis progressively possible. This article assesses the state of the research working toward the remote monitoring of armed conflicts and highlights opportunities to increase the positive societal impact of future research efforts. First, we map the existing literature, categorizing studies in terms of conflict events that are covered, conflict context and scope, techniques, and types of satellite imagery used to identify conflict events. Second, we discuss how these choices affect opportunities to develop applications for human rights, humanitarian, and peacekeeping actors. Third, we provide an outlook, assessing promising paths forward. While much focus has been on high–spatial resolution imagery, we demonstrate why research on freely available satellite images with moderate spatial but high temporal resolution can lead to more scalable and transferable options. We argue that research on such images should be prioritized as it will have a greater positive impact on society, and we discuss what types of applications may soon become feasible through such research. We call for concerted efforts to compile a large dataset of non-sensitive conflict events to accelerate research toward the remote monitoring of armed conflicts and for interdisciplinary collaboration to ensure conflict sensitive monitoring solutions.

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“…Remote sensing has been applied to monitor the various attributes of IDP/refugee camps, including the infrastructure evolution [7], the environment [8][9][10][11], refugee camp expansion [9,[12][13][14][15], and natural hazard vulnerability analysis [16], towards an estimation of the residing populations within the camps [17][18][19], using various interpretation, prediction, classification, and modeling approaches. There are previous works that are specific to dwelling detection in IDP/refugee camp [4,[20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network

et al. 2022

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Earth-observation-based mapping plays a critical role in humanitarian responses by providing timely and accurate information in inaccessible areas, or in situations where frequent updates and monitoring are required, such as in internally displaced population (IDP)/refugee settlements. Manual information extraction pipelines are slow and resource inefficient. Advances in deep learning, especially convolutional neural networks (CNNs), are providing state-of-the-art possibilities for automation in information extraction. This study investigates a deep convolutional neural network-based Mask R-CNN model for dwelling extractions in IDP/refugee settlements. The study uses a time series of very high-resolution satellite images from WorldView-2 and WorldView-3. The model was trained with transfer learning through domain adaptation from nonremote sensing tasks. The capability of a model trained on historical images to detect dwelling features on completely unseen newly obtained images through temporal transfer was investigated. The results show that transfer learning provides better performance than training the model from scratch, with an MIoU range of 4.5 to 15.3%, and a range of 18.6 to 25.6% for the overall quality of the extracted dwellings, which varied on the bases of the source of the pretrained weight and the input image. Once it was trained on historical images, the model achieved 62.9, 89.3, and 77% for the object-based mean intersection over union (MIoU), completeness, and quality metrics, respectively, on completely unseen images.

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Refugee Camp Population Estimates Using Automated Feature Extraction

Cited by 4 publications

References 12 publications

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

Toward the remote monitoring of armed conflicts

Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network

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