# TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines

arXiv: https://arxiv.org/abs/2010.10032 · HTML: https://mlsanity.com/paper/2010.10032/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 28 of 28)

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- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images](https://mlsanity.com/paper/2311.01619/) (2023)
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- [DDOS: The Drone Depth and Obstacle Segmentation Dataset](https://mlsanity.com/paper/2312.12494/) (2023)
- [XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approach](https://mlsanity.com/paper/2407.11771/) (2024)
- [PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images](https://mlsanity.com/paper/2204.07243/) (2022)
- [Vision as Unified Multimodal Generation](https://mlsanity.com/paper/2607.06560/) (2026)
- [STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images](https://mlsanity.com/paper/2108.07944/) (2021)
- [SCD: A Stacked Carton Dataset for Detection and Segmentation](https://mlsanity.com/paper/2102.12808/) (2021)
- [CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset](https://mlsanity.com/paper/2409.20353/) (2024)
- [Automatic High Resolution Wire Segmentation and Removal](https://mlsanity.com/paper/2304.00221/) (2023)
- [Deep Learning in Automated Power Line Inspection: A Review](https://mlsanity.com/paper/2502.07826/) (2025)
- [QuadFormer: Quadruple Transformer for Unsupervised Domain Adaptation in Power Line Segmentation of Aerial Images](https://mlsanity.com/paper/2211.16988/) (2022)
- [DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation](https://mlsanity.com/paper/2304.05821/) (2023)
- [PL-UNeXt: Per-stage Edge Detail and Line Feature Guided Segmentation for Power Line Detection](https://mlsanity.com/paper/2303.04413/) (2023)
- [XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection](https://mlsanity.com/paper/2401.09900/) (2024)
- [Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft](https://mlsanity.com/paper/2407.14352/) (2024)
- [Power Line Aerial Image Restoration under dverse Weather: Datasets and Baselines](https://mlsanity.com/paper/2409.04812/) (2024)
- [PGRID: Power Grid Reconstruction in Informal Developments Using High-Resolution Aerial Imagery](https://mlsanity.com/paper/2412.07944/) (2024)
- [Advanced YOLO-based Real-time Power Line Detection for Vegetation Management](https://mlsanity.com/paper/2503.00044/) (2025)
- [Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation](https://mlsanity.com/paper/2505.22105/) (2025)
- [Comparing Object Detection Models for Electrical Substation Component Mapping](https://mlsanity.com/paper/2512.22454/) (2025)
- [GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure](https://mlsanity.com/paper/2601.13052/) (2026)
- [LLM-as-Judge for Semantic Judging of Powerline Segmentation in UAV Inspection](https://mlsanity.com/paper/2604.05371/) (2026)
- [EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation](https://mlsanity.com/paper/2604.09694/) (2026)
- [SEMIR: Topology-Preserving Graph Minors for Thin-Structure Segmentation](https://mlsanity.com/paper/2606.24935/) (2026)
- [Saliency-Depth Conditioning for Zero-Shot Segmentation of Communication-Tower Components in Cluttered UAV Imagery](https://mlsanity.com/paper/2608.25435/) (2026)

## Abstract (arXiv)

Accurate detection and segmentation of transmission towers~(TTs) and power lines~(PLs) from aerial images plays a key role in protecting power-grid security and low-altitude UAV safety. Meanwhile, aerial images of TTs and PLs pose a number of new challenges to the computer vision researchers who work on object detection and segmentation -- PLs are long and thin, and may show similar color as the background; TTs can be of various shapes and most likely made up of line structures of various sparsity; The background scene, lighting, and object sizes can vary significantly from one image to another. In this paper we collect and release a new TT/PL Aerial-image (TTPLA) dataset, consisting of 1,100 images with the resolution of 3,840×2,160 pixels, as well as manually labeled 8,987 instances of TTs and PLs. We develop novel policies for collecting, annotating, and labeling the images in TTPLA. Different from other relevant datasets, TTPLA supports evaluation of instance segmentation, besides detection and semantic segmentation. To build a baseline for detection and segmentation tasks on TTPLA, we report the performance of several state-of-the-art deep learning models on our dataset. TTPLA dataset is publicly available at https://github.com/r3ab/ttpladataset