Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA

Abstract

The availability and usage of optical very high spatial resolution (VHR) satellite images for efficient support of refugee/IDP (internally displaced people) camp planning and humanitarian aid are growing. In this research, an integrated approach was used for dwelling classification from VHR satellite images, which applied the preliminary results of a convolutional neural network (CNN) model as input data for an object-based image analysis (OBIA) knowledgebased semantic classification method. Unlike standard pixel-based classification methods that usually are applied for the CNN model, our integrated approach aggregates CNN results on separately delineated objects as the basic units of a rule-based classification, to include additional prior-knowledge and spatial concepts in the final instance segmentation. An objectbased accuracy assessment methodology was used to assess the accuracy of the classified dwelling categories on a single object-level. Our findings reveal accuracies of more than 90% for each applied parameter of precision, recall and F1-score. We conclude that integrating the CNN models with the OBIA capabilities can be considered an efficient approach for dwelling extraction and classification, integrating not only sample derived knowledge but also priorknowledge about refugee/IDP camp situations, like dwellings size constraints and additional context.

Mask R‐CNN‐based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid‐19 response in Khartoum, Sudan

“…(2018) reviewed recent developments and conducted initial experiments for selected refugee camps where manually mapped data were available, concluding that full automation is not yet possible. Ghorbanzadeh, Tiede, Dabiri, Sudmanns, and Lang (2018) and Ghorbanzadeh, Tiede, Wendt, Sudmanns, and Lang (2020) showed for a single refugee camp, yet with different VHR satellite sensors and different time‐steps, how CNNs can be coupled with knowledge‐based OBIA approaches. Lu, Koperski, Kwan, and Li (2020) extracted tents in a Syrian refugee camp, comparing their proposed fully convolutional network based on an ImageNet pretrained VGG‐16 network with different existing CNN networks and manually labelled data.…”

Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management

“…There were three steps: (1) to generate sample patches of lodging and non-lodging classes, (2) to create and train the model, and (3) to test the model and report its performance [43,44]. Some studies have been reported that use the CNN algorithm in this software for trees identification and classification [45,46] and dwelling identification [47,48]. In this study, a customized architecture for the CNN was used, which included three hidden layers and one fully connected layer (Figure 2), and it was applied to the three models.…”

Multi-Feature Sample Database for Enhancing Deep Learning Tasks in Operational Humanitarian Applications

“…With promising results obtained by deep learning (DL) in various applications (Ghorbanzadeh, Tiede, Wendt, Sudmanns, & Lang, 2020;Quinn, et al, 2018;Tiede, Wendt, Schwendemann, Alobaidi, & Lang, 2021) humanitarian community adopts to data science techniques as well. This also applies to computer vision, which gradually evolves from static rule-based strategies to a more dynamic, self-adaptable machine learning-based approach.…”