The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform

Richard G Newell - One of the best experts on this subject based on the ideXlab platform.

  • distributed solar photovoltaic Array Location and extent dataset for remote sensing object identification
    Scientific Data, 2016
    Co-Authors: Kyle Bradbury, Raghav Saboo, Timothy L Johnson, Jordan M Malof, Arjun Devarajan, Wuming Zhang, Leslie M Collins, Richard G Newell
    Abstract:

    Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) Arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV Arrays to initiate and develop the process of automatically identifying solar PV Locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. Machine-accessible metadata file describing the reported data (ISA-Tab format)

  • automatic detection of solar photovoltaic Arrays in high resolution aerial imagery
    Applied Energy, 2016
    Co-Authors: Jordan M Malof, Kyle Bradbury, Leslie M Collins, Richard G Newell
    Abstract:

    The quantity of small scale solar photovoltaic (PV) Arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such Arrays, including at a high spatial resolution (e.g., cities, counties, or other small regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV Array Location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135km2) collection of publicly available (Bradbury et al., 2016) aerial imagery, with over 2700 human annotated PV Array Locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV Array detection, but with significant potential for improvement in estimating the precise shape/size of the PV Arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.

Kyle Bradbury - One of the best experts on this subject based on the ideXlab platform.

  • Mapping solar Array Location, size, and capacity using deep learning and overhead imagery
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Jordan M Malof, Kyle Bradbury, Bohao Huang, Artem Stretslov
    Abstract:

    The effective integration of distributed solar photovoltaic (PV) Arrays into existing power grids will require access to high quality data; the Location, power capacity, and energy generation of individual solar PV installations. Unfortunately, existing methods for obtaining this data are limited in their spatial resolution and completeness. We propose a general framework for accurately and cheaply mapping individual PV Arrays, and their capacities, over large geographic areas. At the core of this approach is a deep learning algorithm called SolarMapper - which we make publicly available - that can automatically map PV Arrays in high resolution overhead imagery. We estimate the performance of SolarMapper on a large dataset of overhead imagery across three US cities in California. We also describe a procedure for deploying SolarMapper to new geographic regions, so that it can be utilized by others. We demonstrate the effectiveness of the proposed deployment procedure by using it to map solar Arrays across the entire US state of Connecticut (CT). Using these results, we demonstrate that we achieve highly accurate estimates of total installed PV capacity within each of CT's 168 municipal regions.

  • distributed solar photovoltaic Array Location and extent dataset for remote sensing object identification
    Scientific Data, 2016
    Co-Authors: Kyle Bradbury, Raghav Saboo, Timothy L Johnson, Jordan M Malof, Arjun Devarajan, Wuming Zhang, Leslie M Collins, Richard G Newell
    Abstract:

    Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) Arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV Arrays to initiate and develop the process of automatically identifying solar PV Locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. Machine-accessible metadata file describing the reported data (ISA-Tab format)

  • automatic detection of solar photovoltaic Arrays in high resolution aerial imagery
    Applied Energy, 2016
    Co-Authors: Jordan M Malof, Kyle Bradbury, Leslie M Collins, Richard G Newell
    Abstract:

    The quantity of small scale solar photovoltaic (PV) Arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such Arrays, including at a high spatial resolution (e.g., cities, counties, or other small regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV Array Location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135km2) collection of publicly available (Bradbury et al., 2016) aerial imagery, with over 2700 human annotated PV Array Locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV Array detection, but with significant potential for improvement in estimating the precise shape/size of the PV Arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.

Jordan M Malof - One of the best experts on this subject based on the ideXlab platform.

  • Mapping solar Array Location, size, and capacity using deep learning and overhead imagery
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Jordan M Malof, Kyle Bradbury, Bohao Huang, Artem Stretslov
    Abstract:

    The effective integration of distributed solar photovoltaic (PV) Arrays into existing power grids will require access to high quality data; the Location, power capacity, and energy generation of individual solar PV installations. Unfortunately, existing methods for obtaining this data are limited in their spatial resolution and completeness. We propose a general framework for accurately and cheaply mapping individual PV Arrays, and their capacities, over large geographic areas. At the core of this approach is a deep learning algorithm called SolarMapper - which we make publicly available - that can automatically map PV Arrays in high resolution overhead imagery. We estimate the performance of SolarMapper on a large dataset of overhead imagery across three US cities in California. We also describe a procedure for deploying SolarMapper to new geographic regions, so that it can be utilized by others. We demonstrate the effectiveness of the proposed deployment procedure by using it to map solar Arrays across the entire US state of Connecticut (CT). Using these results, we demonstrate that we achieve highly accurate estimates of total installed PV capacity within each of CT's 168 municipal regions.

  • distributed solar photovoltaic Array Location and extent dataset for remote sensing object identification
    Scientific Data, 2016
    Co-Authors: Kyle Bradbury, Raghav Saboo, Timothy L Johnson, Jordan M Malof, Arjun Devarajan, Wuming Zhang, Leslie M Collins, Richard G Newell
    Abstract:

    Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) Arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV Arrays to initiate and develop the process of automatically identifying solar PV Locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. Machine-accessible metadata file describing the reported data (ISA-Tab format)

  • automatic detection of solar photovoltaic Arrays in high resolution aerial imagery
    Applied Energy, 2016
    Co-Authors: Jordan M Malof, Kyle Bradbury, Leslie M Collins, Richard G Newell
    Abstract:

    The quantity of small scale solar photovoltaic (PV) Arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such Arrays, including at a high spatial resolution (e.g., cities, counties, or other small regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV Array Location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135km2) collection of publicly available (Bradbury et al., 2016) aerial imagery, with over 2700 human annotated PV Array Locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV Array detection, but with significant potential for improvement in estimating the precise shape/size of the PV Arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.

Leslie M Collins - One of the best experts on this subject based on the ideXlab platform.

  • distributed solar photovoltaic Array Location and extent dataset for remote sensing object identification
    Scientific Data, 2016
    Co-Authors: Kyle Bradbury, Raghav Saboo, Timothy L Johnson, Jordan M Malof, Arjun Devarajan, Wuming Zhang, Leslie M Collins, Richard G Newell
    Abstract:

    Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) Arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV Arrays to initiate and develop the process of automatically identifying solar PV Locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. Machine-accessible metadata file describing the reported data (ISA-Tab format)

  • automatic detection of solar photovoltaic Arrays in high resolution aerial imagery
    Applied Energy, 2016
    Co-Authors: Jordan M Malof, Kyle Bradbury, Leslie M Collins, Richard G Newell
    Abstract:

    The quantity of small scale solar photovoltaic (PV) Arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such Arrays, including at a high spatial resolution (e.g., cities, counties, or other small regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV Array Location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135km2) collection of publicly available (Bradbury et al., 2016) aerial imagery, with over 2700 human annotated PV Array Locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV Array detection, but with significant potential for improvement in estimating the precise shape/size of the PV Arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.

Yuliya Tarabalka - One of the best experts on this subject based on the ideXlab platform.

  • End-to-End Learning of Polygons for Remote Sensing Image Classification
    2018
    Co-Authors: Nicolas Girard, Yuliya Tarabalka
    Abstract:

    While geographic information systems typically use polygonal representations to map Earth's objects, most state-of-the-art methods produce maps by performing pixelwise classification of remote sensing images, then vectorizing the outputs. This paper studies if one can learn to directly output a vectorial semantic labeling of the image. We here cast a mapping problem as a polygon prediction task, and propose a deep learning approach which predicts vertices of the polygons outlining objects of interest. Experimental results on the Solar photovoltaic Array Location dataset show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs.

  • IGARSS - End-to-End Learning of Polygons for Remote Sensing Image Classification
    IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
    Co-Authors: Nicolas Girard, Yuliya Tarabalka
    Abstract:

    While geographic information systems typically use polygonal representations to map Earth's objects, most state-of-the-art methods produce maps by performing pixelwise classification of remote sensing images, then vectorizing the outputs. This paper studies if one can learn to directly output a vectorial semantic labeling of the image. We here cast a mapping problem as a polygon prediction task, and propose a deep learning approach which predicts vertices of the polygons outlining objects of interest. Experimental results on the Solar photovoltaic Array Location dataset show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs.