Toward Model-Generated Household Listing in Low- and Middle-Income Countries Using Deep Learning

While governments, researchers, and NGOs are exploring ways to leverage big data sources for sustainable development, household surveys are still a critical source of information for dozens of the 232 indicators for the Sustainable Development Goals (SDGs) in low- and middle-income countries (LMICs). Though some countries’ statistical agencies maintain databases of persons or households for sampling, conducting household surveys in LMICs is complicated due to incomplete, outdated, or inaccurate sampling frames. As a means to develop or update household listings in LMICs, this paper explores the use of machine learning models to detect and enumerate building structures directly from satellite imagery in the Kaduna state of Nigeria. Specifically, an object detection model was used to identify and locate buildings in satellite images. In the test set, the model attained a mean average precision (mAP) of 0.48 for detecting structures, with relatively higher values in areas with lower building density (mAP = 0.65). Furthermore, when model predictions were compared against recent household listings from fieldwork in Nigeria, the predictions showed high correlation with household coverage (Pearson = 0.70; Spearman = 0.81). With the need to produce comparable, scalable SDG indicators, this case study explores the feasibility and challenges of using object detection models to help develop timely enumerated household lists in LMICs

Estimation of Soil Base Cation Weathering Rates with the PROFILE Model to Determine Critical Loads of Acidity for Forested Ecosystems in Pennsylvania, USA: Pilot Application of a Potential National Methodology

Base cation weathering (BCw) rate is one of the most influential yet difficult to estimate parameters in the calculation of critical acid loads of nitrogen (N) and sulfur (S) deposition for terrestrial systems. Only the clay correlation-substrate method, a simple empirical model, has been used for estimating BCw rates for forest ecosystems in the conterminous USA and may not be suitable for application at all sites without calibration or revision. An alternate model, PROFILE, may offer an improved method to estimate BCw rates. It is a transferable, process-based model that simulates the weathering rates of groups of minerals. The objective of this study was to evaluate PROFILE using national datasets as a method to estimate BCw rates for forests in the USA, focusing on Pennsylvania (PA) as the first test state. The model paired with national datasets was successfully applied at 51 forested sites across PA. Weathering rates ranged from 119 to 9,245 eq ha(-1) year(-1) and were consistent with soil properties and regional geology. Comparisons of terrestrial critical acid loads with 2002 N and S deposition showed critical load exceedances at 53 % of the sites. This trial evaluation of PROFILE paired with national datasets in PA establishes that there are sufficient data to support the estimation of BCw rates and determination of critical acid loads for forests in the USA. However, the paired method should be applied in other locations to further evaluate the performance of the model in different regions of the country

Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery

Abstract Background Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative sample. Geosampling is a probability-based, gridded population sampling method that addresses some of these issues by using geographic information system (GIS) tools to create logistically manageable area units for sampling. GIS grid cells are overlaid to partition a country’s existing administrative boundaries into area units that vary in size from 50 m × 50 m to 150 m × 150 m. To avoid sending interviewers to unoccupied areas, researchers manually classify grid cells as “residential” or “nonresidential” through visual inspection of aerial images. “Nonresidential” units are then excluded from sampling and data collection. This process of manually classifying sampling units has drawbacks since it is labor intensive, prone to human error, and creates the need for simplifying assumptions during calculation of design-based sampling weights. In this paper, we discuss the development of a deep learning classification model to predict whether aerial images are residential or nonresidential, thus reducing manual labor and eliminating the need for simplifying assumptions. Results On our test sets, the model performs comparable to a human-level baseline in both Nigeria (94.5% accuracy) and Guatemala (96.4% accuracy), and outperforms baseline machine learning models trained on crowdsourced or remote-sensed geospatial features. Additionally, our findings suggest that this approach can work well in new areas with relatively modest amounts of training data. Conclusions Gridded population sampling methods like geosampling are becoming increasingly popular in countries with outdated or inaccurate census data because of their timeliness, flexibility, and cost. Using deep learning models directly on satellite images, we provide a novel method for sample frame construction that identifies residential gridded aerial units. In cases where manual classification of satellite images is used to (1) correct for errors in gridded population data sets or (2) classify grids where population estimates are unavailable, this methodology can help reduce annotation burden with comparable quality to human analysts

What Is Threatening Forests in Protected Areas? A Global Assessment of Deforestation in Protected Areas, 2001–2018

The protection of forests is crucial to providing important ecosystem services, such as supplying clean air and water, safeguarding critical habitats for biodiversity, and reducing global greenhouse gas emissions. Despite this importance, global forest loss has steadily increased in recent decades. Protected Areas (PAs) currently account for almost 15% of Earth’s terrestrial surface and protect 5% of global tree cover and were developed as a principal approach to limit the impact of anthropogenic activities on natural, intact ecosystems and habitats. We assess global trends in forest loss inside and outside of PAs, and land cover following this forest loss, using a global map of tree cover loss and global maps of land cover. While forests in PAs experience loss at lower rates than non-protected forests, we find that the temporal trend of forest loss in PAs is markedly similar to that of all forest loss globally. We find that forest loss in PAs is most commonly—and increasingly—followed by shrubland, a broad category that could represent re-growing forest, agricultural fallows, or pasture lands in some regional contexts. Anthropogenic forest loss for agriculture is common in some regions, particularly in the global tropics, while wildfires, pests, and storm blowdown are a significant and consistent cause of forest loss in more northern latitudes, such as the United States, Canada, and Russia. Our study describes a process for screening tree cover loss and agriculture expansion taking place within PAs, and identification of priority targets for further site-specific assessments of threats to PAs. We illustrate an approach for more detailed assessment of forest loss in four case study PAs in Brazil, Indonesia, Democratic Republic of Congo, and the United States

Building resilience to extreme weather events in Phoenix: Considering contaminated sites and disadvantaged communities

The interplay of contaminated sites, climate change, and disadvantaged communities are a growing concern worldwide. Worsening extreme events may result in accidental contaminant releases from sites and waste facilities that may impact nearby communities. If such communities are already suffering from environmental, economic, health, or social burdens, they may face disproportionate impacts. Equitable resilience planning to address effects of extreme events requires information on where the impacts may be, when they may occur, and who might be impacted. Because resources are often scarce for these communities, conducting detailed modeling may be cost-prohibitive. By considering indicators for four sources of vulnerability (changing extreme heat conditions, contaminated sites, contaminant transport via wind, and population sensitivities) in one holistic framework, we provide a scientifically robust approach that can assist planners with prioritizing resources and actions. These indicators can serve as screening measures to identify communities that may be impacted most and isolate the reasons for these impacts. Through a transdisciplinary case study conducted in Maricopa County (Arizona, USA), we demonstrate how the framework and geospatial indicators can be applied to inform plans for preparedness, response, and recovery from the effects of extreme heat on contaminated sites and nearby populations. The indicators employed in this demonstration can be applied to other locations with contaminated sites to build community resilience to future climate impacts

CRISPR screen for protein inclusion formation uncovers a role for SRRD in the regulation of intermediate filament dynamics and aggresome assembly.

The presence of large protein inclusions is a hallmark of neurodegeneration, and yet the precise molecular factors that contribute to their formation remain poorly understood. Screens using aggregation-prone proteins have commonly relied on downstream toxicity as a readout rather than the direct formation of aggregates. Here, we combined a genome-wide CRISPR knockout screen with Pulse Shape Analysis, a FACS-based method for inclusion detection, to identify direct modifiers of TDP-43 aggregation in human cells. Our screen revealed both canonical and novel proteostasis genes, and unearthed SRRD, a poorly characterized protein, as a top regulator of protein inclusion formation. APEX biotin labeling reveals that SRRD resides in proximity to proteins that are involved in the formation and breakage of disulfide bonds and to intermediate filaments, suggesting a role in regulation of the spatial dynamics of the intermediate filament network. Indeed, loss of SRRD results in aberrant intermediate filament fibrils and the impaired formation of aggresomes, including blunted vimentin cage structure, during proteotoxic stress. Interestingly, SRRD also localizes to aggresomes and unfolded proteins, and rescues proteotoxicity in yeast whereby its N-terminal low complexity domain is sufficient to induce this affect. Altogether this suggests an unanticipated and broad role for SRRD in cytoskeletal organization and cellular proteostasis