Gas source localization in complex environments is critical for applications such as environmental monitoring, industrial safety, and disaster response. Traditional methods often struggle with the challenges posed by a lack of environmental topography integration, especially when interactions between wind and obstacles distort gas dispersion patterns. In this paper, we propose a deep learning-based approach, which leverages spatial context and environmental mapping to enhance gas source localization. By integrating Simultaneous Localization and Mapping (SLAM) with a U-Net-based model, our method predicts the likelihood of gas source locations by analyzing gas sensor data, wind flow, and topography of the environment represented by a 2D occupancy map. We demonstrate the efficacy of our approach using a wheeled robot equipped with a photoionization detector, a LIDAR, and an anemometer, in various scenarios with dynamic wind fields and multiple obstacles. The results show that our approach can robustly locate gas sources, even in challenging environments with fluctuating wind directions, outperforming conventional methods by utilizing topography contextual information. This study underscores the importance of topographical context in gas source localization and offers a flexible and robust solution for real-world applications. Data and code are publicly available.

Image de-distortion is very important because distortions will degrade the image quality significantly. It can benefit many computational visual media applications that are primarily designed for high-quality images. In order to address this challenging issue, we propose a stacked semantically-guided network, which is the first try on this task. It can capture and restore the distortions around the humans and the adjacent background effectively with the stacked network architecture and the semantically-guided scheme. In addition, a discriminative restoration loss function is proposed to recover different distorted regions in the images discriminatively. As another important effort, we construct a large-scale dataset for image de-distortion. Extensive qualitative and quantitative experiments show that our proposed method achieves a superior performance compared with the state-of-the-art approaches.