Eddie A Bright

A global poverty map derived from satellite data

A global poverty map has been produced at 30arcsec resolution using a poverty index calculated by dividing population count (LandScan 2004) by the brightness of satellite observed lighting (DMSP nighttime lights). Inputs to the LandScan product include satellite-derived land cover and topography, plus human settlement outlines derived from high-resolution imagery. The poverty estimates have been calibrated using national level poverty data from the World Development Indicators (WDI) 2006 edition. The total estimate of the numbers of individuals living in poverty is 2.2 billion, slightly under the WDI estimate of 2.6 billion. We have demonstrated a new class of poverty map that should improve over time through the inclusion of new reference data for calibration of poverty estimates and as improvements are made in the satellite observation of human activities related to economic activity and technology access.

Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape

The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be defined as unplanned, unauthorized, and/or unstructured housing. Techniques for efficiently mapping these settlement boundaries can benefit various decision making bodies. From a remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other types of structures (e.g., industrial, commercial, and formal residential). These spatial characteristics are often captured in high spatial resolution satellite imagery. We analyzed the role of spatial, structural, and contextual features (e.g., GLCM, Histogram of Oriented Gradients, Line Support Regions, Lacunarity) for urban neighborhood mapping, and computed several low-level image features at multiple scales to characterize local neighborhoods. The decision parameters to classify formal-, informal-, and non-settlement classes were learned under Decision Trees and a supervised classification framework. Experiments were conducted on high-resolution satellite imagery from the CitySphere collection, and four different cities (i.e., Caracas, Kabul, Kandahar, and La Paz) with varying spatial characteristics were represented. Overall accuracy ranged from 85% in La Paz, Bolivia, to 92% in Kandahar, Afghanistan. While the disparities between formal and informal neighborhoods varied greatly, many of the image statistics tested proved robust.

Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing.

We describe a method for indexing and retrieving high-resolution image regions in large geospatial data libraries. An automated feature extraction method is used that generates a unique and specific structural description of each segment of a tessellated input image file. These tessellated regions are then merged into similar groups, or sub-regions, and indexed to provide flexible and varied retrieval in a query-by-example environment. The methods of tessellation, feature extraction, sub-region clustering, indexing, and retrieval are described and demonstrated using a geospatial library representing a 153 km2 region of land in East Tennessee at 0.5 m per pixel resolution.

Machine learning approaches for high-resolution urban land cover classification: a comparative study

The proliferation of several machine learning approaches makes it difficult to identify a suitable classification technique for analyzing high-resolution remote sensing images. In this study, ten classification techniques were compared from five broad machine learning categories. Surprisingly, the performance of simple statistical classification schemes like maximum likelihood and Logistic regression over complex and recent techniques is very close. Given that these two classifiers require little input from the user, they should still be considered for most classification tasks. Multiple classifier systems is a good choice if the resources permit.

Large-Scale geospatial indexing for image-based retrieval and analysis

We describe a method for indexing and retrieving high-resolution image regions in large geospatial data libraries. An automated feature extraction method is used that generates a unique and specific structural description of each segment of a tessellated input image file. These tessellated regions are then merged into similar groups and indexed to provide flexible and varied retrieval in a query-by-example environment.

Geospatial image mining for nuclear proliferation detection: Challenges and new opportunities

With increasing understanding and availability of nuclear technologies, and increasing persuasion of nuclear technologies by several new countries, it is increasingly becoming important to monitor the nuclear proliferation activities. There is a great need for developing technologies to automatically or semi-automatically detect nuclear proliferation activities using remote sensing. Images acquired from earth observation satellites is an important source of information in detecting proliferation activities. High-resolution remote sensing images are highly useful in verifying the correctness, as well as completeness of any nuclear program. DOE national laboratories are interested in detecting nuclear proliferation by developing advanced geospatial image mining algorithms. In this paper we describe the current understanding of geospatial image mining techniques and enumerate key gaps and identify future research needs in the context of nuclear proliferation.

Locally adaptive, spatially explicit projection of US population for 2030 and 2050

Significance Oak Ridge National Laboratory (ORNL) is a leader in population distribution and dynamics research, particularly in developing gridded population datasets. For this study, ORNL researchers leverage their expertise in intelligent dasymetric modeling to construct large-scale, national level, spatially distributed population projections for the contiguous United States. The model presented here departs from other spatially explicit projection models by accounting for socioeconomic and cultural characteristics that influence spatial population growth at smaller scales, while still projecting population at a large scale. The resulting projected population distribution can be exploited for long-term urban and infrastructure planning, and scientific modeling for climate change. Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census’s projection methodology, with the US Census’s official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.

Overhead image statistics

Statistical properties of high-resolution overhead images representing different land use categories are analyzed using various local and global statistical image properties based on the shape of the power spectrum, image gradient distributions, edge co-occurrence, and inter-scale wavelet coefficient distributions. The analysis was performed on a database of high-resolution (1 meter) overhead images representing a multitude of different downtown, suburban, commercial, agricultural and wooded exemplars. Various statistical properties relating to these image categories and their relationship are discussed. The categorical variations in power spectrum contour shapes, the unique gradient distribution characteristics of wooded categories, the similarity in edge co-occurrence statistics for overhead and natural images, and the unique edge co-occurrence statistics of downtown categories are presented in this work. Though previous work on natural image statistics has showed some of the unique characteristics for different categories, the relationships for overhead images are not well understood. The statistical properties of natural images were used in previous studies to develop prior image models, to predict and index objects in a scene and to improve computer vision models. The results from our research findings can be used to augment and adapt computer vision algorithms that rely on prior image statistics to process overhead images, calibrate the performance of overhead image analysis algorithms, and derive features for better discrimination of overhead image categories.

High resolution urban feature extraction for global population mapping using high performance computing

The advent of high spatial resolution satellite imagery like Quick Bird (0.6 meter) and IKONOS (1 meter) has provided a new data source for high resolution urban land cover mapping. Extracting accurate urban regions from high resolution images has many applications and is essential to the population mapping efforts of Oak Ridge National Laboratorys (ORNL) LandScan population distribution program. This paper discusses an automated parallel algorithm that has been implemented on a high performance computing environment to extract urban regions from high resolution images using texture and spectral features.

Wal-Mart from Space : A New Source for Land Cover Change Validation

We introduce an event data set of the location and opening dates for 3,043 Wal-Mart stores as a means for validating land-cover change-related products at medium (28.5 m) to coarse (250 m to 1 km) resolutions throughout the conterminous United States. Strengths of the Wal-Mart validation data set include: construction within most U.S. ecoregions, building footprints greater than a hectare in size, and construction dates that span much of the remote sensing record (1962 to 2004). We built the data set by geo-coding Wal-Mart addresses, establishing opening dates, and geolocating the footprints of 30 stores using online free highresolution (4 m) imagery. Disturbance events were evident at 25 Wal-Marts within a single scene of the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS-beta) forest disturbance product. In addition, we found clear disturbance signals within two 16-day vegetation index time series: the 250 m Moderate Resolution Imaging Spectroradiometer normalized difference product (MOD44C) and the 1 km enhanced vegetation index product (MOD13A2).