Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Geography”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Contemporary income inequality outweighs historic redlining in shaping intra-urban heat disparities in Los Angeles

The roots of intra-urban heat disparity in the U.S. often trace back to historical discriminatory practices, such as redlining, which categorized neighborhoods by race or ethnicity. In this study, we compare the relative impacts of historic redlining and current income inequality on thermal disparities in Los Angeles. A key innovation of our work is the use of land surface temperature data from the ECOSTRESS instrument aboard the International Space Station, enabling us to capture diurnal trends in urban thermal disparities. Our findings reveal that present-day income inequality is a stronger predictor of heat burden than the legacy of redlining. Additionally, land surface temperature disparities exhibit a seasonal hysteresis effect, intensifying during extreme heat events by 5−7 °C. Sociodemographic analysis highlights that African-American and Hispanic populations in historically and economically disadvantaged areas are often the most vulnerable. Our findings suggest that while the legacy of redlining may persist, the present-day heat disparities are not necessarily an immutable inheritance, where targeted investments and interventions can pave the way for a more thermally just future for these communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A baseline structure inventory with critical attribution for the US and its territories

Leveraging high performance computing, remote sensing, geographic data science, machine learning, and computer vision, Oak Ridge National Laboratory has partnered with Federal Emergency Management Agency (FEMA) to build a baseline structure inventory covering the US and its territories to support disaster preparedness, response, and recovery. The dataset contains more than 125 million structures with critical attribution, and is ready to be used by federal agencies, local government and first responders to accelerate on-the-ground response to disasters, further identify vulnerable areas, and develop strategies to enhance the resilience of critical structures and communities. Data can be freely and openly accessed through Figshare data repository, ESRI’s Living Atlas or FEMA’s Geodata platform.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

ORBITaL-Net: A labeled training library for large-scale building feature extraction

Over the course of several years, nearly 1.5 million building outlines have been created from approximately 128,000 training tiles covering roughly 7,000 km 2 of very high-resolution multispectral overhead imagery, primarily dated between 2010 and 2020. This dataset, dubbed the Oak Ridge Building Image and TrAining Label Net (ORBITaL-Net), is designed for machine learning applications and is global in scope, with samples drawn from 72 countries across North America, South America, Africa, Europe, and Asia. ORBITaL-Net captures a great diversity in geographic setting, structural characteristics, land use (urban and rural), terrain, and imagery conditions. While the labeled building outlines are themselves valuable, the dataset’s true strength lies in the pairing of these labels with corresponding reference imagery, which is being released for open source use. Similar to SpaceNet and Replicable AI For Microplanning (ramp), this building outline dataset will allow the larger computer vision community from academia, government, and industry the opportunity to develop robust, scalable, and generalizable geospatial machine learning techniques. Unlike SpaceNet and ramp, which offer high resolution labels and imagery primarily for large urban cities, ORBITaL-Net is not focused on training samples from heavily populated areas but instead aims to capture the innate variability of conditions present in both the physical environment and imagery collections.

Geography↗
DOE OSTI2023

Integration of urban science and urban climate adaptation research: opportunities to advance climate action

There is a growing recognition that responding to climate change necessitates urban adaptation. We sketch a transdisciplinary research effort, arguing that actionable research on urban adaptation needs to recognize the nature of cities as social networks embedded in physical space. Given the pace, scale and socioeconomic outcomes of urbanization in the Global South, the specificities and history of its cities must be central to the study of how well-known agglomeration effects can facilitate adaptation. The proposed effort calls for the co-creation of knowledge involving scientists and stakeholders, especially those historically excluded from the design and implementation of urban development policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Global divergence in urban demographic change and migration patterns

Cities are central to economic development, climate adaptation and social stability, yet globally consistent evidence on how city populations are changing remains limited. Here we analyze annual age- and sex-structured population estimates for more than 10,000 cities worldwide from 2000 to 2020 and show that urban demographic change was highly uneven. Globally, the ratio of children and older adults to working-age adults declined from 0.87 to 0.59, but smaller cities remained consistently younger than larger cities, especially in Africa. We also find pronounced spatial variation in urban sex ratios, including strong male surpluses in parts of the Middle East and North Africa, consistent with patterns of labor migration. Finally, we estimate that 45% of urban population growth was attributable to net migration and 55% to natural increase. These results show that national averages can obscure substantial differences between cities, and highlight the value of globally consistent city-level demographic estimates for understanding regional demographic change and informing locally tailored urban planning.

development studies↗
DOE OSTI2018

Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria

Buildings in the developing world are inadequately mapped. Lack of such critical geospatial data adds unnecessary challenges to locating and reaching a large segment of the world’s most vulnerable population, impeding sustainability goals ranging from disaster relief to poverty reduction. Use of volunteered geographic information (VGI) has emerged as a widely accepted source to fill such voids. Despite its promise, availability of building maps for developing countries significantly lags behind demand. We present a new approach, coupling deep convolutional neural networks (CNNs) with VGI for automating building map generation from high-resolution satellite images for Kano state, Nigeria. Specifically, we trained a CNN with VGI building outlines of limited quality and quantity and generated building maps for a 50,000 km 2 area. Resulting maps are in strong agreement with existing settlement maps and require a fraction of the manual input needed for the latter. The VGI-based maps will provide support across multiple facets of socioeconomic development in Kano state, and demonstrates potential advancements in current mapping capabilities in resource constrained countries.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Divide and conquer: separating the two probabilities in seismic phase picking

There are two fundamental probabilities in the seismic phase picking process—the probability of the existence of a seismic phase (detection probability) and the probability associated with the phase arrival time estimation (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labelled phase arrival time and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks), and the height of the peaks are taken as ‘probability’ of the picks. Here, we show that this ‘probability’ represents neither the detection nor the timing probability because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multiclass classifier to obtain a probability mass function to address the timing probability. This new approach can make the deep learning-based phase picking process more interpretable and provide options to logically control seismic monitoring workflows.

58 GEOSCIENCES↗
DOE OSTI2018

Dynamic Data-Driven SAR Image Reconstruction Using Multiple GPUs

According to the article, the reconstruction of nxn-pixel synthetic aperture radar (SAR) imagery using a backprojection algorithm incurs O(n 2 · m) cost, where m is the number of pulses. This paper presents dynamic data-driven multiresolution algorithms to speed up SAR backprojection on multiple graphics processing units (GPUs). A critical part of this spatially variant reconstruction process is load balancing, which circumvents asymmetric work assignment. Fine-tuned algorithms for GPUs are presented as a part of improving running time. Communication between processors is overlapped with GPU calculation to reduce communication time.

Engineering↗
DOE OSTI2023

Salinity–induced limits to mangrove canopy height

Aim: Mangrove canopy height is a key metric to assess tidal forests' resilience in the face of climate change. In terrestrial forests, tree height is primarily determined by water availability, plant hydraulic design, and disturbance regime. However, the role of water stress remains elusive in tidal environments, where saturated soils are prevalent, and salinity can substantially affect the soil water potential. Location: Global. Time Period: The canopy height dataset provides a global snapshot of the maximum mangrove height geographical distribution for the year 2000. Climate and environmental variables extend over the period 1970–2018. Major Taxa Studied: Mangroves. Methods: We use global observations of maximum canopy height, species richness, air temperature, and seawater salinity—a proxy of soil water salt concentration—to explore the causal link between salinity and mangrove stature. Results: Our findings suggest that salt stress limits mangrove height. High salinity favours more salt-tolerant species, narrowing the spectrum of viable traits. Highly salt-tolerant mangroves have evolved to cope with high salt concentrations in the soil, but this adaptation comes at a cost. They typically have lower rates of photosynthesis and growth, resulting in reduced productivity and smaller stature compared to more salt-sensitive mangrove species. This suggests a causal link between salinity, biodiversity, and tree height, where high salinity selects for more salt-tolerant species that tend to be less productive and shorter. Conclusions: We hypothesize that the salinity-induced limit to mangrove canopy height is the direct result of a reduction of primary productivity, an increment in the risk of xylem cavitation, and an indirect consequence of the decrease in biodiversity. As sea-level rise enhances coastal salinisation, failure to account for these effects can lead to incorrect estimates of future carbon stocks in Tropical coastal ecosystems and endanger preservation efforts.

54 ENVIRONMENTAL SCIENCES↗