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Upcoming special issue in Urban Planning (cogitatio): Citizen-Centric Urban Planning Through Participatory Sensing Data and Digital Tools
https://www.cogitatiopress.com/urbanplanning/issue/futureissues#i530
Abstract submission deadline in December.

Future Issues | Urban Planning
Urban Planning is a new international peer-reviewed open access journal of urban studies

SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
Zewei Yang, Zengqi Peng, Jun Ma
https://arxiv.org/abs/2509.13956

SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
Autonomous parking is a critical component for achieving safe and efficient urban autonomous driving. However, unstructured environments and dynamic interactions pose significant challenges to autonomous parking tasks. To address this problem, we propose SEG-Parking, a novel end-to-end offline reinforcement learning (RL) framework to achieve interaction-aware autonomous parking. Notably, a specialized parking dataset is constructed for parking scenarios, which include those without interference…

Urban Diaspora VI 🏮
城市放逐 VI 🏮
📷 Nikon FE
🎞️Ilford FP4 Plus, expired 1994
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Complexity Analysis of a Bicriteria Directed Multimodal Transportation Network Design Problem
Dominik Leib, Susanne Fritzler, Neele Leith\"auser
https://arxiv.org/abs/2507.07894

Complexity Analysis of a Bicriteria Directed Multimodal Transportation Network Design Problem
In this paper, we address a bicriteria network design problem that arises from practical applications in urban and rural public transportation planning. We establish the problem's complexity and demonstrate inapproximability results, highlighting the inherent difficulties in finding optimal solutions. Additionally, we identify special cases where approximability can be achieved, providing valuable insights for practitioners. Our proofs leverage complexity results related to directed network des…

Co-Win: Joint Object Detection and Instance Segmentation in LiDAR Point Clouds via Collaborative Window Processing
Haichuan Li, Tomi Westerlund
https://arxiv.org/abs/2507.19691 …

Co-Win: Joint Object Detection and Instance Segmentation in LiDAR Point Clouds via Collaborative Window Processing
Accurate perception and scene understanding in complex urban environments is a critical challenge for ensuring safe and efficient autonomous navigation. In this paper, we present Co-Win, a novel bird's eye view (BEV) perception framework that integrates point cloud encoding with efficient parallel window-based feature extraction to address the multi-modality inherent in environmental understanding. Our method employs a hierarchical architecture comprising a specialized encoder, a window-based b…

On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments
Armen Manukyan, Hrant Khachatrian, Edvard Ghukasyan, Theofanis P. Raptis
https://arxiv.org/abs/2507.19653

On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments
We study the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in central Rome. We use a real measurement set of 1,664 user-equipments (UEs) and six nominal base-station (BS) sites. Using these fixed positions we systematically vary the main simulation parameters, including path depth, diffuse/specular/refraction flags, carrier frequency, as well as antenna's properties like its altitude, radiation pattern, and orientation. Simulator fidelity is scored for each base station via Sp…