Reducing the gap between general purpose data and aerial images in concentrated solar power plants

• URL: http://arxiv.org/abs/2508.00440v1
• Date: Fri, 01 Aug 2025 08:57:02 GMT
• Title: Reducing the gap between general purpose data and aerial images in concentrated solar power plants
• Authors: M. A. Pérez-Cutiño, J. Valverde, J. Capitán, J. M. Díaz-Báñez,
• Abstract summary: We introduce AerialCSP, a high-quality synthetic dataset for aerial inspection of CSP plants.<n>We benchmark multiple models on AerialCSP, establishing a baseline for CSP-related vision tasks.<n>We demonstrate that pretraining on AerialCSP significantly improves real-world fault detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the context of Concentrated Solar Power (CSP) plants, aerial images captured by drones present a unique set of challenges. Unlike urban or natural landscapes commonly found in existing datasets, solar fields contain highly reflective surfaces, and domain-specific elements that are uncommon in traditional computer vision benchmarks. As a result, machine learning models trained on generic datasets struggle to generalize to this setting without extensive retraining and large volumes of annotated data. However, collecting and labeling such data is costly and time-consuming, making it impractical for rapid deployment in industrial applications. To address this issue, we propose a novel approach: the creation of AerialCSP, a virtual dataset that simulates aerial imagery of CSP plants. By generating synthetic data that closely mimic real-world conditions, our objective is to facilitate pretraining of models before deployment, significantly reducing the need for extensive manual labeling. Our main contributions are threefold: (1) we introduce AerialCSP, a high-quality synthetic dataset for aerial inspection of CSP plants, providing annotated data for object detection and image segmentation; (2) we benchmark multiple models on AerialCSP, establishing a baseline for CSP-related vision tasks; and (3) we demonstrate that pretraining on AerialCSP significantly improves real-world fault detection, particularly for rare and small defects, reducing the need for extensive manual labeling. AerialCSP is made publicly available at https://mpcutino.github.io/aerialcsp/.

Related papers

• Unlocking Thermal Aerial Imaging: Synthetic Enhancement of UAV Datasets [1.2289361708127877]
  We introduce a novel procedural pipeline for generating synthetic thermal images from an aerial perspective.<n>Our method integrates arbitrary object classes into existing thermal backgrounds.<n>We show that thermal detectors outperform their visible-light-trained counterparts.
  arXiv  Detail & Related papers  (2025-07-09T12:34:56Z)
• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis [57.249817395828174]
  We propose a scalable framework combining pseudo-synthetic renderings from 3D city-wide meshes with real, ground-level crowd-sourced images.<n>The pseudo-synthetic data simulates a wide range of aerial viewpoints, while the real, crowd-sourced images help improve visual fidelity for ground-level images.<n>Using this hybrid dataset, we fine-tune several state-of-the-art algorithms and achieve significant improvements on real-world, zero-shot aerial-ground tasks.
  arXiv  Detail & Related papers  (2025-04-17T17:57:05Z)
• Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling [61.95831392879045]
  Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE)<n>Despite significant progress, their outputs may still exhibit issues such as amplified noise, incorrect white balance, or unnatural enhancements when deployed in real world applications.<n>A key challenge is the lack of diverse, large scale training data that captures the complexities of low-light conditions and imaging pipelines.<n>We propose a novel image signal processing (ISP) driven data synthesis pipeline that addresses these challenges by generating unlimited paired training data.
  arXiv  Detail & Related papers  (2025-04-16T15:53:53Z)
• Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI [3.4764766275808583]
  Cloud formations often obscure optical satellite-based monitoring of the Earth's surface. We propose a novel synthetic dataset for cloud optical thickness estimation. We leverage for obtaining reliable and versatile cloud masks on real data.
  arXiv  Detail & Related papers  (2023-11-23T14:28:28Z)
• SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction [2.554658234030785]
  This study introduces a novel approach that aims to learn the geographical link between multimodal features via self-supervised contrastive learning. The proposed approach has undergone rigorous testing on two distinct large-scale datasets.
  arXiv  Detail & Related papers  (2023-08-07T13:44:44Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• LARD - Landing Approach Runway Detection -- Dataset for Vision Based Landing [2.7400353551392853]
  We present a dataset of high-quality aerial images for the task of runway detection during approach and landing phases. Most of the dataset is composed of synthetic images but we also provide manually labelled images from real landing footages. This dataset paves the way for further research such as the analysis of dataset quality or the development of models to cope with the detection tasks.
  arXiv  Detail & Related papers  (2023-04-05T08:25:55Z)
• A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth [61.90504318229845]
  This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene. A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
  arXiv  Detail & Related papers  (2022-06-13T19:14:06Z)
• Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks [1.2891210250935146]
  We propose a data augmentation methodology based on Generative Adrial Networks (GANs) to generate a variety of synthetic light curves from variable stars. The classification accuracy of variable stars is improved significantly when training with synthetic data and testing with real data.
  arXiv  Detail & Related papers  (2022-05-13T16:39:54Z)
• AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration [16.032316550612336]
  We present AirDet, which is free of fine-tuning by learning class relation with support images. AirDet achieves comparable or even better results than the exhaustively finetuned methods, reaching up to 40-60% improvements on the baseline. We present evaluation results on real-world exploration tests from the DARPA Subterranean Challenge.
  arXiv  Detail & Related papers  (2021-12-03T06:41:07Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)

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.