Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark

Zhang, Xinyu; Yu, Junsheng; Wang, Lizhe; Han, Wei; Feng, Ruyi; Fan, Runyu; Wang, Sheng

doi:10.3390/rs14194729

Cited by 12 publications

(4 citation statements)

References 78 publications

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“…The deformable vision transformer consistently attends to the most important semantic features, significantly improving performance. Zhang et al (Zhang et al, 2022) contributed to the field by creating the "Road Datasets in Complex Mountain Environments (RDCME)" dataset, proposing the Light Roadformer model for forest road extraction, which utilizes a transformer based on self-attention units. Post-processing techniques are applied to reduce errors, resulting in a mIoU of 89.5% for RDCME.…”

Section: Related Workmentioning

confidence: 99%

Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation

Kelesakis,

Marthoglou,

Tokmaktsi

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Sustainable forest and emergency management require comprehensive data on the forest road network and its condition. This paper presents the final framework of the INFOROAD project (https://inforoad.karteco.gr/), which integrates cutting-edge remote sensing and machine learning technologies for automated periodic extraction and monitoring of the forest road network. The framework includes gravel road extraction, road graph extraction, and gravel road condition monitoring, with a focus on the periurban forest in Thessaloniki. The road extraction employs the U-TAE network architecture, with a proposed modification using inverted residual blocks for improved accuracy. Road graph extraction involves creating a graph from road segmentation output or OSM data, enabling efficient road segment analysis. Gravel-road width calculation utilizes road segmentation results and a series of image processing steps, while road condition monitoring employs ML/AI classification algorithms. Worldview 3 high-resolution satellite images and various auxiliary data sources (e.g. DEM) are used as input, including field measurements for the training of classification algorithms. Results showcase the effectiveness of the proposed framework, with gravel road extraction accuracy improved by the modified U-TAE model. Regarding gravel road condition monitoring, algorithms achieving satisfactory results are identified, despite the challenges that arise, due to the significant surface and texture variations in forest and agricultural roads. A WebGIS platform facilitates information presentation, user interaction, and management of geospatial information, supporting various functionalities such as layer management and spatial data visualization. The INFOROAD project represents a significant advancement in leveraging technology for sustainable forest road management and emergency preparedness. Future steps may involve further enhancements and adaptations for improvement of results.

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Section: Related Workmentioning

confidence: 99%

Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation

Kelesakis,

Marthoglou,

Tokmaktsi

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…a pyramidal architecture of a deformable vision transformer, for the extraction of road networks from satellite images. The deformable vision transformer consistently attends to the most important semantic features, significantly improving performance.. Zhang, X. et al, 7 created a new dataset, "Road Datasets in Complex Mountain Environments (RDCME)" with 775 RGB Quickbird images with 0,61 m resolution, which include forest roads in complex environments. They also propose the Light Roadformer model for extracting forest roads, which uses a transformer based on a self-attention unit that attends to the road boundaries.…”

Section: Related Workmentioning

confidence: 99%

A holistic framework for forestry and rural road detection based on satellite imagery and deep semantic segmentation

Kelesakis,

Marthoglou,

GRAMMALIDIS

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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The role of forest and rural road network is very important and multifaceted for: a) the exploitation of forests/fields and movement of the staff involved in their management and protection, b) access to existing technical facilities, c) tourism and recreation and d) harvesting and transport of products. Hence, it is important to systematically record and monitor the network status, including its road segment status and important Points of Interest. Since in most cases, such detection and monitoring procedures require manual and tedious procedures, the INFOROAD project (https://inforoad.karteco.gr/) (Project code: KMP6-0079153), implemented under the framework of the Action "Investment Plans of Innovation" of the Operational Program "Central Macedonia 2014 2020", that is co-funded by the European Regional Development Fund and Greece", is developing an innovative methodology and tools for automated extraction, mapping and status monitoring of forest and rural road network, by combining remote sensing data with state-of-the-art deep learning approaches. Specifically, automated procedures will be developed for: a) road extraction, b) road network graph detection and c) road segment condition monitoring, from very high-resolution multispectral satellite images using innovative semantic segmentation and classification based on deep learning. From a research perspective, these problems are very challenging, as forest and agricultural roads -as opposed to asphalt roads -exhibit more significant surface and texture variations, while only few related datasets exist. For this reason, additional data, which is necessary for the training and evaluation of the deep learning algorithms, will also be collected, e.g., by UAVs equipped with RGB cameras that can also provide accurate DSM information, manual inspection or other free sources. The associated dataset that will be created for the pilot application of the project in Seich-Sou forest, Thessaloniki, will be made freely available to boost future research on the field. The display and management of this information by relevant stakeholders (forest authorities, public, etc.) will be performed by an online Geographical Information System (WebGIS).

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“…RDCME [51] is a multi-spectral image dataset collected from the northwest region of China. The images in this dataset have a resolution of 0.61 m/pixel.…”

Section: Road Datasets In Complex Mountain Environments (Rdcme)mentioning

confidence: 99%

“…We conducted comprehensive evaluations of the superiority of GLNet by comparing it with other well-known segmentation methods and state-of-the-art road extraction methods on various evaluation metrics, including OA, Precision, Recall, IoU, and F1 Score. The models used for comparison include DANet [49], Deeplabv3+ [22], PSPNet [66], Segformer-b5 [67], UNet [68], Light Roadformer [51], D-LinkNet [69], RADANet [70], and SPBAM-LinkNet [71]. To ensure fair comparisons, all models were evaluated under the same experimental conditions.…”

Section: Ablation Studymentioning

confidence: 99%

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Wang,

Cai,

et al. 2023

Remote Sensing

Self Cite

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Road extraction is crucial in urban planning, rescue operations, and military applications. Compared to traditional methods, using deep learning for road extraction from remote sensing images has demonstrated unique advantages. However, previous convolutional neural networks (CNN)-based road extraction methods have had limited receptivity and failed to effectively capture long-distance road features. On the other hand, transformer-based methods have good global information-capturing capabilities, but face challenges in extracting road edge information. Additionally, existing excellent road extraction methods lack validation for the Chinese region. To address these issues, this paper proposes a novel road extraction model called the global–local information fusion network (GLNet). In this model, the global information extraction (GIE) module effectively integrates global contextual relationships, the local information extraction (LIE) module accurately captures road edge information, and the information fusion (IF) module combines the output features from both global and local branches to generate the final extraction results. Further, a series of experiments on two different Chinese road datasets with geographic robustness demonstrate that our model outperforms the state-of-the-art deep learning models for road extraction tasks in China. On the CHN6-CUG dataset, the overall accuracy (OA) and intersection over union (IoU) reach 97.49% and 63.27%, respectively, while on the RDCME dataset, OA and IoU reach 98.73% and 84.97%, respectively. These research results hold significant implications for road traffic, humanitarian rescue, and environmental monitoring, particularly in the context of the Chinese region.

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Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark

Cited by 12 publications

References 78 publications

Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation

Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation

A holistic framework for forestry and rural road detection based on satellite imagery and deep semantic segmentation

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

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