Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning

Salas, Joaquín; Vera, Pablo; Zea-Ortiz, Marivel; Villaseñor, Elio-Atenogenes; Pulido, Dagoberto; Figueroa, Alejandra

doi:10.3390/rs13183603

Cited by 5 publications

(1 citation statement)

References 46 publications

(88 reference statements)

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“…Alhichri et al [18] enriched the EfficientNet-B3-based model by adding an Attention module to further increase the classification performance. Salas et al [20] used EfficientNet-B3 to map satellite images to census data in order to characterize vulnerable communities at the residential block level and, therefore, localize poor areas where poverty reduction policies can be implemented. According to the published papers, no instance of EfficientNet used as encoder for a segmentation model has yet been proposed.…”

Section: Efficientnetmentioning

confidence: 99%

Integrating EfficientNet into an HAFNet Structure for Building Mapping in High-Resolution Optical Earth Observation Data

Ferrari

Dell’Acqua

Zhang³

et al. 2021

Remote Sensing

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Automated extraction of buildings from Earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, and policy-making. Combining data from different sensors, such as high-resolution multispectral images (HRI) and light detection and ranging (LiDAR) data, has shown great potential in building extraction. Deep learning (DL) is increasingly used in multi-modal data fusion and urban object extraction. However, DL-based multi-modal fusion networks may under-perform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multi-modal features. Recently, a hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset, including co-located Very-High-Resolution (VHR) optical images and light detection and ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model over-parametrization, which inevitably leads to long training times and heavy computational load. In this paper, the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

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Section: Efficientnetmentioning

confidence: 99%

Integrating EfficientNet into an HAFNet Structure for Building Mapping in High-Resolution Optical Earth Observation Data

Ferrari

Dell’Acqua

Zhang³

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

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Integration of Datasets Toward Slum Identification: Local Implementation of the IDEAMAPS Framework

Cabrera Zamora,

Juárez Carrillo,

Ramírez Santiago

et al. 2024

Remote Sensing and Digital Image Processing

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A data-driven approach to mapping multidimensional poverty at residential block level in Mexico

Zea-Ortiz,

Vera,

Salas

et al. 2024

Environ Dev Sustain

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Accurate, inexpensive and granular human poverty assessments are critical for data-driven policy decision-making. This research proposes a novel approach to computing poverty scores utilizing multispectral satellite images and indices calculated from census reference values. We show how this approach can leverage standard and sparse survey-based multidimensional poverty assessments at the municipal level to develop a deep learning architecture to obtain poverty scores at the residential block level. This method has the distinctive feature that the obtained inference corresponds to Multidimensional Measurement of Poverty generated by CONEVAL, the Mexican agency responsible for measuring poverty. We provide a reliable alternative to survey-based approaches with an $$R^2$$ R 2 of $$0.802\pm 0.022$$ 0.802 ± 0.022 for the lack of housing quality and spaces dimension. A convolutional neural network trained on multispectral satellite images and the lack of housing quality and spaces dimension, which is regressed from census reference variables corresponding to lack of water, electricity, sewage, concrete floor, toilet and occupancy level obtains an $$R^2$$ R 2 of 0.753. These results represent a significant step forward in including machine learning techniques to provide reliable information at reduced costs and a higher spatiotemporal frequency than traditional person-to-person surveys.

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Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning

Cited by 5 publications

References 46 publications

Integrating EfficientNet into an HAFNet Structure for Building Mapping in High-Resolution Optical Earth Observation Data

Integrating EfficientNet into an HAFNet Structure for Building Mapping in High-Resolution Optical Earth Observation Data

Integration of Datasets Toward Slum Identification: Local Implementation of the IDEAMAPS Framework

A data-driven approach to mapping multidimensional poverty at residential block level in Mexico

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