Equitable development through deep learning

Doupé, Patrick; Bruzelius, Emilie; Faghmous, James H.; Ruchman, Samuel G.

doi:10.1145/3001913.3001921

Cited by 19 publications

(9 citation statements)

References 14 publications

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“…Previous studies have revealed that the detailed characteristics of various landscapes can be well reflected by these 6 visible and invisible bands [54]. Figure 5 presents the probability density distribution of population count in the ground-truth samples and the example RS image patches that correspond to various population counts.…”

Section: Landsat-5 Rs Imagerymentioning

confidence: 97%

“…In order to highlight large errors, absolute errors are squared in RMSE. Since MAE and RMSE are not as understandable, the percentage errors (%MAE and %RMSE) assessing the proportion of the error to the actual value are also presented [54]. These 4 error metrics evaluate the absolute and percentage estimation error together.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…Human activities can lead to distinct spatial landscapes, which inversely constrain how people live, produce, and travel, providing the possibility of consistent and sustainable population estimation with RS imagery as auxiliary data without the problems mentioned above [53]. However, the raw RS imagery is highly unstructured, and its association with population count is complex and nonlinear, making it challenging to construct a mapping from raw RS imagery to population count [54]. An emerging supervised deep learning approach, convolutional neural networks (CNN), which are capable of extracting the hidden hierarchical structures of RS images [55], have shown outstanding performance in obtaining knowledge from RS images in the domain of geography (e.g., land use classification [56], spatial interpolation [57], and poverty mapping [58]).…”

Section: Introductionmentioning

confidence: 99%

“…A few studies have tried to estimate population counts from RS imagery directly. Doupe proposed the use of a VGG-like network to estimate population density in Tanzanian and Kenya from Landsat images and achieved remarkable performance and generalizability [54]. Robinson regarded the population estimation task as a classification problem and used a similar VGG-like network to classify RS image patches into 14 population density levels.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

Zhuang

Yan

et al. 2021

Remote Sensing

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Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas.

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Section: Landsat-5 Rs Imagerymentioning

confidence: 97%

Section: Accuracy Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

Zhuang

Yan

et al. 2021

Remote Sensing

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“…Abitbol and Karsai [32] applied a CNN model to predict inhabited tiles' socioeconomic status and projected the class discriminative activation maps onto the original images, interpreting the estimation of wealth in terms of urban topology. To date, daytime imagery and deep neural networks have been widely applied to predict various socioeconomic indicators such as population [33][34][35], poverty distribution [15,18,36], and urbanization [6,37]. Despite the convenience and scalability, these studies depend largely on data-intensive CNNs and require large volumes of ground-truth labels to supervise the training process.…”

Section: Detection Of Economic-related Visual Patterns From Daytime Satellite Imagery Via Deep Learningmentioning

confidence: 99%

Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery

Liu

Bai

et al. 2021

Remote Sensing

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The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.

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Counting the Uncountable: Deep Semantic Density Estimation from Space

Rodríguez

Wegner

2019

Lecture Notes in Computer Science

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We propose a new method to count objects of specific categories that are significantly smaller than the ground sampling distance of a satellite image. This task is hard due to the cluttered nature of scenes where different object categories occur. Target objects can be partially occluded, vary in appearance within the same class and look alike to different categories. Since traditional object detection is infeasible due to the small size of objects with respect to the pixel size, we cast object counting as a density estimation problem. To distinguish objects of different classes, our approach combines density estimation with semantic segmentation in an end-to-end learnable convolutional neural network (CNN). Experiments show that deep semantic density estimation can robustly count objects of various classes in cluttered scenes. Experiments also suggest that we need specific CNN architectures in remote sensing instead of blindly applying existing ones from computer vision.

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Equitable development through deep learning

Cited by 19 publications

References 14 publications

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery

Counting the Uncountable: Deep Semantic Density Estimation from Space

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