Search NASA⌕ Search

SEARCH · Search NASA

Results for “urban mapping”

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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Seasat views North America, the Caribbean, and Western Europe with imaging radar

Forty-one digitally correlated Seasat synthetic-aperture radar images of land areas in North America, the Caribbean, and Western Europe are presented to demonstrate this microwave orbital imagery. The characteristics of the radar images, the types of information that can be extracted from them, and certain of their inherent distortions are briefly described. Each atlas scene covers an area of 90 X 90 kilometers, with the exception of the one that is the Nation's Capital. The scenes are grouped according to salient features of geology, hydrology and water resources, urban landcover, or agriculture. Each radar image is accompanied by a corresponding image in the optical or near-infrared range, or by a simple sketch map to illustrate features of interest. Characteristics of the Seasat radar imaging system are outlined.

Ford, J. P.↗
NASA NTRS1980

Applications of HCMM satellite data

The thermal properties of Lake Ontario as they relate to water equality, lake hydrology and energy exchange were investigated as well as the urban heat island problem in selected areas adjacent to the lake. The HCMM thermal sensor was fully calibrated for several underflight data. Actual surface water temperature maps were generated for all of Lake Ontario using the calibration procedure developed. Major water quality changes associated with the thermal bar as located by HCMM thermal data were observed from satellite and aerial data and verified by ground truth.

Source record↗
NASA NTRS2017

GPS/Optical/Inertial Integration for 3D Navigation Using Multi-Copter Platforms

In concert with the continued advancement of a UAS traffic management system (UTM), the proposed uses of autonomous unmanned aerial systems (UAS) have become more prevalent in both the public and private sectors. To facilitate this anticipated growth, a reliable three-dimensional (3D) positioning, navigation, and mapping (PNM) capability will be required to enable operation of these platforms in challenging environments where global navigation satellite systems (GNSS) may not be available continuously. Especially, when the platform's mission requires maneuvering through different and difficult environments like outdoor opensky, outdoor under foliage, outdoor-urban and indoor, and may include transitions between these environments. There may not be a single method to solve the PNM problem for all environments. The research presented in this paper is a subset of a broader research effort, described in [1]. The research is focused on combining data from dissimilar sensor technologies to create an integrated navigation and mapping method that can enable reliable operation in both an outdoor and structured indoor environment. The integrated navigation and mapping design is utilizes a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), a monocular digital camera, and three short to medium range laser scanners. This paper describes specifically the techniques necessary to effectively integrate the monocular camera data within the established mechanization. To evaluate the developed algorithms a hexacopter was built, equipped with the discussed sensors, and both hand-carried and flown through representative environments. This paper highlights the effect that the monocular camera has on the aforementioned sensor integration scheme's reliability, accuracy and availability.

Dill, Evan T.↗
NASA NTRS2023

Using Satellite Data to Characterize Land Surface Processes in Morocco

This study endeavors to produce a comprehensive land cover map for Morocco, addressing the absence of such a detailed map in the country. Our research encompasses ecological and climatic aspects specific to Morocco, while the methods used can be adapted to various regions and countries, considering their unique climatic conditions and land cover types. A combination of MODIS and Landsat datasets was employed to create a 5 km resolution Land Use and Land Cover (LULC) map for the entire nation. The process involved the aggregation and advanced processing of these datasets using surface processes algorithms. The resulting LULC map is the first of its kind for Morocco, shedding light on land cover distribution nationwide. It shows that approximately 13.5% of the country is covered by forests, predominantly in the Atlas and Rif mountains, Rabat–Sale, and the southern regions. Grasslands occupy over 16% of the study area, mainly in the north-east and west. Urban areas, including major cities like Casablanca, Rabat, and Marrakech, span nearly 3400 km². Moreover, large areas of shrublands and bare lands are evident across the country, while agricultural lands account for almost 20% of the national territory, mainly in the interior plains and north-western Atlantic coast. This study forms a crucial basis for ecological and climatic research in Morocco and serves as a valuable reference for various disciplines such as agriculture, natural resource management, and climate modeling. The mapping of biophysical parameters for each land cover class is a key feature of our research, and these parameters will be instrumental in a subsequent study examining the impact of urban development on surface climate in Morocco. Overall, our study underscores the importance of understanding biophysical parameters in addressing environmental and societal challenges.

Mohammed Thaiki↗
DOE OSTI2023

Bias Correction in Urban Building Energy Modeling for Chicago Using Machine Learning

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.

Chowdhury, Shovan↗
DOE OSTI2026

Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach

High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Rural land mobile radio market assessment and satellite and terrestrial system concepts

The market for satellite-based mobile radio in the rural U.S. is evaluated, summarizing the results of two NASA-funded studies reported by Anderson et al. and Hornstein. The study aims are listed, and the results are presented in tables, graphs, and maps and discussed. Space systems are found to be competitive with land-based systems, providing superior service at lower subscriber charges, but having limited compatibility with urban cellular mobile-radio systems. Of the three system concepts evaluated from a technological standpoint (direct-to-mobile, mobile-translator, and hybrid), the mobile-translator concept is considered most cost effective, at least within the constraints assumed in the study.

Stevenson, S.↗
NASA NTRS2017

Global navigation satellite system reflectometry (GNSS-R) algorithms for wetland observations

It is important to closely monitor the state of the world’s wetlands, as climate change and human encroachment in a rapid global urbanization trend threaten to cause large-scale wetland collapse. Because wetlands are often difficult to observe in situ, remote sensing is the only viable way to map wetland extent globally. However, current remote sensing methods suffer limitations in capturing wetland extent, and more importantly, wetland dynamics at appropriate spatial and temporal scales. GNSSReflectometry could help fill the current observation gap, as experimental data show that ground-reflected GNSS signals are very sensitive to changes in inundated areas. Furthermore, because this technique only requires a custom developed receiver and antenna system, a constellation of such instruments can potentially be launched at relatively low cost, providing global observations at sub-daily intervals. One challenge remains, however, which is quantitatively formulating the geophysical product of reflections over the land surface in various states of inundation. Here, we use a novel reflection dataset, derived from the SMAP radar receiver, to elucidate the sensitivity of reflections to small land surface features and their seasonal variations. Additionally, we quantify the dynamic range of reflections over both open and closed wetlands, and suggest an algorithm for wetland type classification.

UNKNOWN↗
NASA NTRS1973

Land use mapping and modelling for the Phoenix Quadrangle

The author has identified the following significant results. Comparison of 9 x 9 MSS band images and color composites made from bands 4, 5, and 6 showing vegetated areas near Phoenix during the summer, fall, and winter seasons aided in definitely establishing that certain land areas were being used as agricultural land and not as rangeland. Agricultural land, which appeared to be fallow, idle, or not irrigated, often became more readily identifiable as agricultural land when comparing different images of identical land areas which have been affected by seasonal vegetation changes. Experimentation with the Bausch and Lomb Zoom Transferscope using MSS images of identical areas in the same spectral band from different time periods, with a quick flip method of alternately viewing the frame areas, enabled rapid detection of a major land use change from agricultural to urban use on the northwest fringe of the metropolitan Phoenix area. The best results in this case were obtained when comparing MSS band 5 images. Examination of MSS transparencies and color composites allowed further updating of a map of land use change in the Phoenix Quadrangle.

Place, J. L.↗
NASA NTRS1976

Use of radar in urban studies

The use of side-looking airborne radar for urban studies is reviewed with attention given to the work of Moore (1969) and Lewis (1968) which may be summarized as follows: (1) linear elements of the transportation net were easily defined, (2) gross patterns of industry, residential and open space land were identified, but it was not possible to map the land use boundaries in great detail, (3) commercial land areas were often difficult to identify, and (4) multiple polarized imagery was helpful in correctly interpreting the total scene. It is found that the sensitivity of radar to surface roughness and the availability of multiple wavelength data allow the discrimination of variations in the surface roughness of intra-urban areas. An L-band imaging radar (25 cm; 1215-1225 GHz) of 25 m resolution will be operating from satellite altitudes in 1978 and will increase the availability of radar data.

Bryan, M. L.↗
NASA NTRS1995

Urban remote sensing applications: TIMS observations of the City of Scottsdale

A research program has been initiated between Arizona State University and the City of Scottsdale, Arizona to study the potential applications of TIMS (Thermal Infrared Multispectral Scanner) data for urban scene classification, desert environmental assessment, and change detection. This program is part of a long-term effort to integrate remote sensing observations into state and local planning activities to improve decision making and future planning. Specific test sites include a section of the downtown Scottsdale region that has been mapped in very high detail as part of a pilot program to develop an extensive GIS database. This area thus provides excellent time history of the evolution of the city infrastructure, such as the timing and composition of street repavement. A second area of study includes the McDowell intensive study by state and local agencies to assess potential sites for urban development as well as preservation. These activities are of particular relevance as the Phoenix metropolitan area undergoes major expansion into the surrounding desert areas. The objectives of this study in urban areas are aimed at determining potential applications of TIMS data for classifying and assessing land use and surface temperatures. Land use centers on surface impermeability studies for storm runoff assessment and pollution control. These studies focus on determining the areal abundance of urban vegetation and undeveloped soil. Highly experimental applications include assessment and monitoring of pavement condition. Temperature studies focus on determining swimming pool area and temperature for use in monitoring evaporating and urban water consumption. These activities are of particular relevance as the Phoenix metropolitan area undergoes major expansion into the surrounding desert area.

Christensen, Philip R.↗
NASA NTRS1970

Climatology of urban regional systems

The combining of remote sensing technologies to urban-regional energy climatology is studied. It was found to be three dimensional with a mosaic urban surface, each smaller surface with its own radiant and thermal properties. Urban patterns of radiant exchange were found to be constantly changing during diurnal and annual cycles. Results were derived from Barbados data using remote methods for monitoring and mapping radiation. Isoline maps of terrestrial radiation patterns were made generalizing the minute patterns of the scan image.

Pease, R. W.↗
NASA NTRS1978

LANDSAT image differencing as an automated land cover change detection technique

Image differencing was investigated as a technique for use with LANDSAT digital data to delineate areas of land cover change in an urban environment. LANDSAT data collected in April 1973 and April 1975 for Austin, Texas, were geometrically corrected and precisely registered to United States Geological Survey 7.5-minute quadrangle maps. At each pixel location reflectance values for the corresponding bands were subtracted to produce four difference images. Areas of major reflectance differences are isolated by thresholding each of the difference images. The resulting images are combined to obtain an image data set to total change. These areas of reflectance differences were found, in general, to correspond to areas of land cover change. Information on areas of land cover change was incorporated into a procedure to mask out all nonchange areas and perform an unsupervised classification only for data in the change areas. This procedure identified three broad categories: (1) areas of high reflectance (construction or extractive), (2) changes in agricultural areas, and (3) areas of confusion between agricultural and other areas.

Stauffer, M. L.↗
NASA NTRS1987

Image Processing

A new spinoff product was derived from Geospectra Corporation's expertise in processing LANDSAT data in a software package. Called ATOM (for Automatic Topographic Mapping), it's capable of digitally extracting elevation information from stereo photos taken by spaceborne cameras. ATOM offers a new dimension of realism in applications involving terrain simulations, producing extremely precise maps of an area's elevations at a lower cost than traditional methods. ATOM has a number of applications involving defense training simulations and offers utility in architecture, urban planning, forestry, petroleum and mineral exploration.

Source record↗
NASA NTRS2024

Baltimore Energy & Infrastructure: Assessing Urban Heat Vulnerability in Baltimore Neighborhoods to Inform Transportation Resiliency Planning Efforts

Across the world, climate change is altering cities and their local climate systems. Following global trends of rising mean temperatures, Baltimore, Maryland is projected to experience more frequent extreme heat events. Exacerbated by Urban Heat Island (UHI) effects, residents of the city face high temperatures from limited tree canopy, highly paved and impervious surfaces, limited air flow, and high concentrations of localized emissions. Urban heat is dispersed asymmetrically, with historically marginalized populations experiencing disproportionately severe heat. Recognizing the impacts of extreme urban heat, the Maryland Transit Administration (MTA) collaborated with us, the Spring 2024 Goddard Energy and Infrastructure NASA DEVELOP team to assess urban impacts of MTA users and assets and explore opportunities to enhance resiliency planning. We evaluated the feasibility of NASA Earth observations (EOs) in visualizing heat vulnerability in Baltimore City and County. We mapped UHI and extreme heat using remote sensing observations, tailored a Heat Priority Score (HPS) to analyze heat data in tandem with socioeconomic data from American Community Survey (ACS), and utilized SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model to estimate MTA riders’ thermal comfort. NASA EOs served to quantify the distribution and severity of extreme heat at the block group level, which was found to correlate with a lack of vegetation and presence of densely built-up surroundings. The HPS revealed that extreme heat events most negatively impact communities that exhibit social vulnerability through indicators such as age, race, and income. These findings will inform neighborhood resiliency plans for the MTA that can be incorporated into their Adaptation Resiliency Toolbox (ART).

Baltimore,↗
NASA NTRS2012

An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to the phenology, solar-view geometry, and atmospheric condition etc. factors but not actual landcover difference. Finally, we will compare the classification results from screened and unscreened training samples to assess the improvement achieved by cleaning up the training samples. Keywords:

Tan, Bin↗
NASA NTRSDate not supplied

Fault Detection and Performance Monitoring of Propellers in Electric UAV

Unmanned aerial vehicles (UAVs) are used in various industries such as agriculture and logistics, to name but a few, where their applications are beyond basic mapping, surveillance, and photography. In near future, UAVs are expected to be used in package delivery service and larger electric vertical takeoff and landing vehicles will be employed for urban air mobility applications (air taxi). Thus, several electric propulsion systems will enter the low-altitude airspace with frequent take offs and landings. To achieve state-of-the-art safety standards under such high traffic density, UAVs will require in-time fault detection and performance monitoring of critical powertrain components. This work focuses on propeller blade performance and damage detection in electric UAVs. Propellers are the fastest moving component in an UAV; even a minor defect in the propeller blades could cause performance deterioration, with consequent challenges in flying through the planned trajectory or adhere to the safety requirements of the operation. Monitoring and updating aerodynamic efficiency of each rotor would therefore enable the detection of off-nominal propeller conditions thus magnifying the state-awareness of powertrain monitoring systems based on the acquired electrical signals. We use an extended Kalman filter-based parameter estimation algorithm that incorporates time history responses from UAV powertrain in conjunction with a full powertrain system model. Propeller fault detection is achieved by incorporating the aerodynamic parameters of propeller into the powertrain model. The proposed technique is successfully validated with numerical simulations.

Unmanned aerial vehicles↗