Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

Ren, Simiao; Hu, Wei; Bradbury, Kyle; Harrison‐Atlas, Dylan; Valeri, Laura Malaguzzi; Murray, Brian C.; Malof, Jordan M.

doi:10.1016/j.apenergy.2022.119876

Cited by 17 publications

(6 citation statements)

References 214 publications

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“…Intersection over union (IoU) is a number that quantifies the degree of overlap between two boxes. In the case of object detection and segmentation, IoU evaluates the overlap of the ground truth and prediction region [31]. This evaluation method was preferred over other methods (e.g., average precision, F1 score) because of the connotation of this work.…”

Section: Data Collectionmentioning

confidence: 99%

Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

Giussani,

Wilczynski,

Zandonella Callegher

et al. 2024

Sustainability

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Locating and quantifying photovoltaic (PV) installations is a time-consuming and labor-intensive process, but it is necessary for monitoring their distribution. In the absence of existing data, the use of aerial imagery and automated detection algorithms can improve the efficiency and accuracy of the data collection process. This study presents a machine learning approach for the analysis of PV installations in urban areas based on less complex and resource-intensive models to target the challenge of data scarcity. The first objective of this work is to develop a model that can automatically detect PV installations from aerial imagery and test it based on the case study of Crevillent, Spain. Subsequently, the work estimates the PV capacity in Crevillent, and it compares the distribution of PV installations between residential and industrial areas. The analysis utilizes machine learning techniques and existing bottom-up data to assess land use and building typology for PV installations, identifying deployment patterns across the town. The proposed approach achieves an accuracy of 67% in detecting existing PV installations. These findings demonstrate that simple machine learning models still provide a reliable and cost-effective way to obtain data for decision-making in the fields of energy and urban planning, particularly in areas with limited access to existing data. Combining this technology with bottom-up data can lead to more comprehensive insights and better outcomes for urban areas seeking to optimize and decarbonize their energy supply while minimizing economic resources.

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Section: Data Collectionmentioning

confidence: 99%

Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

Giussani,

Wilczynski,

Zandonella Callegher

et al. 2024

Sustainability

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“…11,12 In general, work related to image quality has focused on capturing and recording images for human viewing, whereas work in computer vision has taken relatively high quality images as its starting point without giving significant thought to the image chains producing them. Given the increasing prevalence of automated image processing for self-driving vehicles and analysis of remote sensing data, [13][14][15][16] the relationship between image quality and algorithm performance represents an increasingly important element of end-to-end sensing systems.…”

Section: Introductionmentioning

confidence: 99%

Image quality and object detection performance of convolutional neural networks

Bergstrom

Messinger

2023

Pattern Recognition and Tracking XXXIV

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Significant bodies of research have explored the topics of computer vision and image quality, but research on the intersection of these two disciplines remains limited. Additionally, evidence suggests that image quality as determined by the human visual system may differ from image quality as determined by the performance of computer vision algorithms. Furthermore, most of the research on the relationships between image quality and computer vision performance has focused on single-label image classification and has not considered tasks such as semantic segmentation or object detection. Here, we consider the relationship between three primary image quality factors–resolution, blur, and noise–and the performance of deep-learning based object detection models. To do so, we examine the impacts of these image quality variables on mean average precision (mAP) of object detection models, evaluating the performance of models trained on only high-quality images as well as models fine tuned on lower quality images. Additionally, we map our primary image quality variables to the terms used in the General Image Quality Equation (GIQE)–namely ground sample distance (GSD), relative edge response (RER), and signal to noise ratio (SNR)–and assess the suitability of the GIQE functional form for modeling object detector performance in the presence of significant image distortions.

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“…The data may not be publicly available, may be incomplete, or may not be of a sufficiently high resolution (Stowell et al, 2020). Recent research has demonstrated the potential of using satellite imagery to fill the data gaps by monitoring energy systems at unprecedented frequencies and scale (Donti and Kolter, 2021; Ren et al, 2022). Two remaining challenges to detecting climate objects at scale include (1) a lack of large datasets with labeled data for relevant applications, and (2) the difficulty of applying these techniques across diverse geographic domains.…”

Section: Introductionmentioning

confidence: 99%

Closing the domain gap: blended synthetic imagery for climate object detection

Kornfein,

Willard,

Tang

et al. 2023

Environ. Data Science

Self Cite

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Accurate geospatial information about the causes and consequences of climate change, including energy systems infrastructure, is critical to planning climate change mitigation and adaptation strategies. When up-to-date spatial data on infrastructure is lacking, one approach to fill this gap is to learn from overhead imagery using deep-learning-based object detection algorithms. However, the performance of these algorithms can suffer when applied to diverse geographies, which is a common case. We propose a technique to generate realistic synthetic overhead images of an object (e.g., a generator) to enhance the ability of these techniques to transfer across diverse geographic domains. Our technique blends example objects into unlabeled images from the target domain using generative adversarial networks. This requires minimal labeled examples of the target object and is computationally efficient such that it can be used to generate a large corpus of synthetic imagery. We show that including these synthetic images in the training of an object detection model improves its ability to generalize to new domains (measured in terms of average precision) when compared to a baseline model and other relevant domain adaptation techniques.

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Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

Cited by 17 publications

References 214 publications

Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

Image quality and object detection performance of convolutional neural networks

Closing the domain gap: blended synthetic imagery for climate object detection

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