Overgrown roadside vegetation poses a danger to road users by obstructing the visibility of the road and potentially obscuring signs or other traffic participants. Thus, regulations clearly define the height above the road that must not be obscured. As manually identifying such incidents is time-consuming, we propose 3D Clearance Control: a pipeline that automatically detects vegetation in need of trimming. Our system is based on LiDAR point clouds, which give access to accurate position and height information. It comprises four main steps: the semantic segmentation of the point cloud, the aggregation of scans within a scene, the estimation of road boundaries, and the creation of the volume representing the clearance gauge. We developed a modular process to perform a comprehensive evaluation combining different segmentation models and road boundary approximation methods. We mea-sure the accuracy and computing times on three widely used street-level datasets: SemanticKITTI, NuScenes, and PandaSet. We achieved an mIoU of 67.6 on our annotated test scenes and a speed increase of 52.7% compared to previous systems.

Given a text query on a controversial topic, the task of Image Retrieval for Argumentation is to rank images according to how well they can be used to support a discussion on the topic. An important subtask therein is to determine the stance of the retrieved images, i.e., whether an image supports the pro or con side of the topic. In this paper, we conduct a comprehensive reproducibility study of the state of the art as represented by the CLEF'22 Touché lab and an in-house extension of it. Based on the submitted approaches, we developed a unified and modular retrieval process and reimplemented the submitted approaches according to this process. Through this unified reproduction (which also includes models not previously considered), we achieve an effectiveness improvement in argumentative image detection of up to 0.832 [email protected] However, despite this reproduction success, our study also revealed a previously unknown negative result: for stance detection, none of the reproduced or new approaches can convincingly beat a random baseline. To understand the apparent challenges inherent to image stance detection, we conduct a thorough error analysis and provide insight into potential new ways to approach this task.