A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

• URL: http://arxiv.org/abs/2209.03726v1
• Date: Thu, 8 Sep 2022 11:42:53 GMT
• Title: A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata
• Authors: Gabriel Kasmi, Yves-Marie Saint-Drenan, David Trebosc, Rapha\"el Jolivet, Jonathan Leloux, Babacar Sarr, Laurent Dubus
• Abstract summary: We propose a dataset containing aerial images, annotations, and segmentation masks. We provide installation metadata for more than 28,000 installations. We provide ground truth segmentation masks for 13,000 installations, including 7,000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8,000 installations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale PV installations are deployed at an unprecedented pace, and their integration into the grid can be challenging since public authorities often lack quality data about them. Overhead imagery is increasingly used to improve the knowledge of residential PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be easily transferred from one region or data source to another due to differences in image acquisition. To address this issue known as domain shift and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, annotations, and segmentation masks. We provide installation metadata for more than 28,000 installations. We provide ground truth segmentation masks for 13,000 installations, including 7,000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8,000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.

Related papers

• SolarFormer: Multi-scale Transformer for Solar PV Profiling [7.686020113962378]
  SolarFormer is designed to segment solar panels from aerial imagery, offering insights into their location and size. Our model leverages low-level features and incorporates an instance query mechanism to enhance the localization of solar PV installations. Our experiments consistently demonstrate that our model either matches or surpasses state-of-the-art models.
  arXiv  Detail & Related papers  (2023-10-30T22:22:01Z)
• Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces [49.44971010149331]
  We propose a novel dataset that includes accurate and dense ground-truth labels at high resolution. Our acquisition pipeline leverages a novel deep space-time stereo framework. The dataset is composed of 606 samples collected in 85 different scenes.
  arXiv  Detail & Related papers  (2023-01-19T18:59:28Z)
• DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping [0.0]
  We build on existing work to propose an automated PV registry pipeline. This pipeline automatically generates a dataset recording all distributed PV installations' location, area, installed capacity, and tilt angle. We propose an unsupervised method based on the it Registre national d'installation (RNI), that centralizes all individual PV systems aggregated at communal level. We deploy our model on 9 French it d'epartements covering more than 50 000 square kilometers, providing the largest mapping of distributed PV panels with this level of detail to date.
  arXiv  Detail & Related papers  (2022-07-15T13:23:24Z)
• SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting [0.0]
  There are few publicly available standardized benchmark datasets for image-based solar forecasting. We introduce SKIPP'D -- a SKy Images and Photovoltaic Power Generation dataset. The dataset contains quality-controlled down-sampled sky images and PV power generation data ready-to-use for short-term solar forecasting using deep learning.
  arXiv  Detail & Related papers  (2022-07-02T21:52:50Z)
• VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments [49.82314641876602]
  We present a new dataset named VPAIR. The dataset was recorded on board a light aircraft flying at an altitude of more than 300 meters above ground. The dataset covers a more than one hundred kilometers long trajectory over various types of challenging landscapes.
  arXiv  Detail & Related papers  (2022-05-23T18:50:08Z)
• MAT: Mask-Aware Transformer for Large Hole Image Inpainting [79.67039090195527]
  We present a novel model for large hole inpainting, which unifies the merits of transformers and convolutions. Experiments demonstrate the state-of-the-art performance of the new model on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T06:36:17Z)
• HIPA: Hierarchical Patch Transformer for Single Image Super Resolution [62.7081074931892]
  This paper presents HIPA, a novel Transformer architecture that progressively recovers the high resolution image using a hierarchical patch partition. We build a cascaded model that processes an input image in multiple stages, where we start with tokens with small patch sizes and gradually merge to the full resolution. Such a hierarchical patch mechanism not only explicitly enables feature aggregation at multiple resolutions but also adaptively learns patch-aware features for different image regions.
  arXiv  Detail & Related papers  (2022-03-19T05:09:34Z)
• ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond [76.35955924137986]
  We propose a Vision Transformer Advanced by Exploring intrinsic IB from convolutions, i.e., ViTAE. ViTAE has several spatial pyramid reduction modules to downsample and embed the input image into tokens with rich multi-scale context. We obtain the state-of-the-art classification performance, i.e., 88.5% Top-1 classification accuracy on ImageNet validation set and the best 91.2% Top-1 accuracy on ImageNet real validation set.
  arXiv  Detail & Related papers  (2022-02-21T10:40:05Z)
• Computer Vision Tool for Detection, Mapping and Fault Classification of PV Modules in Aerial IR Videos [0.0]
  We develop a computer vision tool for the semi-automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107842 PV modules from thermographic videos of seven different PV plants.
  arXiv  Detail & Related papers  (2021-06-14T11:38:13Z)
• TransVG: End-to-End Visual Grounding with Transformers [102.11922622103613]
  We present a transformer-based framework for visual grounding, namely TransVG, to address the task of grounding a language query to an image. We show that the complex fusion modules can be replaced by a simple stack of transformer encoder layers with higher performance.
  arXiv  Detail & Related papers  (2021-04-17T13:35:24Z)
• An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data [6.127530266825379]
  This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries.
  arXiv  Detail & Related papers  (2020-12-07T13:45:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.