Accelerating satellite image based large-scale settlement detection with GPU

Patlolla, Dilip R.; Bright, Eddie A; Weaver, Jeanette; Cheriyadat, Anil M.

doi:10.1145/2447481.2447487

Cited by 10 publications

(10 citation statements)

References 11 publications

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“…HOGGLCM is the concatenation of two independent features and can be described as a texturebased feature combining the use of filters and pixel values to identify/characterize spatial structure and composition (Dalal and Triggs 2005;Patlolla et al 2012) TEXTONS is similar to HOGGLCM and functions by utilizing filters for the purpose of capturing textural variation and orientation (Malik et al 2001;Patlolla et al 2015) Differing from both HOGGLCM and TEXTONS is VEGIND, which derives a variety of spectral indices and concatenates each individual index into a single feature vector (see Appendix A, Table A1) to differentiate vegetation from non-vegetation.…”

Section: Methodsmentioning

confidence: 99%

“…In turn, each model was applied to the entire image to produce a binary output representing the presence/absence of human settlement ( Figure 6). For further information regarding the HPC strategy employed for this process consult Patlolla et al (2012) and for a detailed description of the work flow refer to Weber et al (2018). This entire process was reproduced 20 times for each of the four seasonal images.…”

Section: Methodsmentioning

confidence: 99%

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The impact of seasonality on multi-scale feature extraction techniques

Moore

McKee

2019

International Journal of Digital Earth

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A key component in constructing a broad-scale, gridded population dataset is fine resolution geospatial data accurately depicting the extent of human activity. Analogous datasets are often developed using a wide range of methods and classification techniques, including the use of spatial features, spectral features, or the coupling of both to identify the presence of man-made structures from high-resolution satellite imagery. By using spatial and textural-based descriptors to generate highresolution settlement layers for two dissimilar regions at the peak of seasonal disparity, this study attempts to quantify the influence of seasonality on the accuracy of a supervised, multi-scale, feature extraction framework for automated delineation of human settlement. Results generated by numerous models are evaluated against a reference dataset allowing for assessment of seasonal and feature differences in the context of accuracy. Global or regional mapping of human settlement requires the assemblage of high-resolution satellite images with variegated acquisition characteristics (season, sun elevation, off-nadir, etc.) to produce a cloud-free composite image from which features are extracted. Results of this study suggest an emphasis on imagery criteria, in particular acquisition date, could improve classification accuracy when mapping human settlement at scale.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The impact of seasonality on multi-scale feature extraction techniques

Moore

McKee

2019

International Journal of Digital Earth

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“…The mask was obtained from the LandScan project team at Oak Ridge National Laboratory, where it underwent extensive quality control to ensure its accuracy. More information on the development of the settlement layer may be found in Cheriyadat et al (2007) and Patlolla et al (2012). By this process, the models were iteratively trained using different subsets of imagery until reaching a result wherein all of the previously recorded potential training points were accurately classified according to their original label.…”

Section: Settlement Characterizationmentioning

confidence: 99%

Electricity consumption patterns within cities: application of a data-driven settlement characterization method

Chowdhury

Weaver

Weber

et al. 2019

International Journal of Digital Earth

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Urban areas presently consume around 75% of global primary energy supply, which is expected to significantly increase in the future due to urban growth. Having sustainable, universal energy access is a pressing challenge for most parts of the globe. Understanding urban energy consumption patterns may help to address the challenges to urban sustainability and energy security. However, urban energy analyses are severely limited by the lack of urban energy data. Such datasets are virtually non-existent for the developing countries. As per current projections, most of the new urban growth is bound to occur in these data-starved regions. Hence, there is an urgent need of research methods for monitoring and quantifying urban energy utilization patterns. Here, we apply a data-driven approach to characterize urban settlements based on their formality, which is then used to assess intraurban urban energy consumption in Johannesburg, South Africa; Sana'a, Yemen; and Ndola, Zambia. Electricity is the fastest growing energy fuel. By analyzing the relationship between the settlement types and the corresponding nighttime light emission, a proxy of electricity consumption, we assess the differential electricity consumption patterns. Our study presents a simple and scalable solution to fill the present data void to understand intra-city electricity consumption patterns.

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“…1) designed for identifying critical infrastructures from large-scale satellite or aerial imagery to assess vulnerable population. We exploit the parallel processing capability of GPUs to present GPUfriendly algorithms for robust and efficient detection of settlements from large-scale high-resolution satellite imagery (Patlolla et al 2012). Feature descriptor generation is an expensive (computationally demanding), but a key step in automated scene analysis.…”

Section: Settlement Mapping Tool (Smtool)mentioning

confidence: 99%

“…We could thus achieve GPU-based high speed computation of multiple feature descriptors-multiscale Histogram of Oriented Gradients (HOG) (Patlolla et al 2012), Gray Level Co-Occurrence Matrix (GLCM) Contrast, local pixel intensity statistics, Texture response (local Texton responses to a set of oriented filters at each pixel) (Patlolla et al 2015), Dense Scale Invariant Feature Transform (DSIFT), Vegetation Indices (NDVI), Line Support Regions (extraction of straight line segments from an image by grouping spatially contiguous pixels with consistent orientations), Band Ratios (a digital image-processing technique that enhances contrast between features by dividing a measure of reflectance for the pixels in one image band by the measure of reflectance for the pixels in the other image band) etc. Once, the features are computed, a linear SVM is used to classify settlement and non-settlements.…”

Section: Settlement Mapping Tool (Smtool)mentioning

confidence: 99%

Cyber-Infrastructure for Data-Intensive Geospatial Computing

Karthik

Sorokine

Patlolla

et al. 2018

Earth Observation Open Science and Innovation

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With the recent advent of heterogeneous High-performance Computing (HPC) to handle EO "Big Data" workloads, there is a need for a unified Cyber-infrastructure (CI) platform that can bridge the best of many HPC worlds. In this chapter, we discuss such a CI platform being developed at Geographic Information Science and Technology (GIST) group using novel and innovative techniques, and emerging technologies that are scalable to large-scale supercomputers. The CI platform utilizes a wide variety of computing such as GPGPU, distributed, real-time and cluster computing, which are being brought together architecturally to enable datadriven analysis, scientific understanding of earth system models, and research collaboration. This development addresses the need for close integration of EO and other geospatial information in the face of growing volumes of the data, and facilitates spatio-temporal analysis of disparate and dynamic data streams. Horizontal scalability and linear throughput are supported in the heart of the platform itself. It is being used to support very broad application areas, ranging from high-resolution settlement mapping, national bioenergy infrastructure to urban information and mobility systems. The platform provides spatio-temporal decision support capabilities in planning, policy and operational missions for US federal agencies. Also, the platform is designed to be functionally and technologically sustainable for continued support of the US energy and environment mission for the coming decades.

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Accelerating satellite image based large-scale settlement detection with GPU

Cited by 10 publications

References 11 publications

The impact of seasonality on multi-scale feature extraction techniques

The impact of seasonality on multi-scale feature extraction techniques

Electricity consumption patterns within cities: application of a data-driven settlement characterization method

Cyber-Infrastructure for Data-Intensive Geospatial Computing

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