Search: “urban”

arXiv:2610.00729v1 Announce Type: new Abstract: This paper explores a reward-based policy to achieve zero-shot transfer between source and target environments with completely different observation spa

arXiv:2609.40333v1 Announce Type: new Abstract: Self-supervised learning draws inspiration from infant visual development, yet standard training pipelines bear little resemblance to it: images are ind

arXiv:2609.38392v1 Announce Type: new Abstract: Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to includ

arXiv:2609.38977v1 Announce Type: cross Abstract: Neural surrogates have emerged as fast alternatives to the numerical simulation of three-dimensional turbulence. However, training them at high resolu

arXiv:2408.02689v3 Announce Type: replace-cross Abstract: Traffic forecasting uses recent measurements by sensors installed at chosen locations to forecast the future road traffic. Existing work eithe

arXiv:2609.36066v1 Announce Type: cross Abstract: Open-world aerial object-goal search is a foundational yet challenging task, requiring aerial agents to autonomously explore large-scale, unstructured

arXiv:2606.19122v2 Announce Type: replace Abstract: Sidewalks in the real world are crowded, cluttered, and less structured than roads, making 3D occupancy prediction a key ingredient for the safe nav

arXiv:2609.32732v1 Announce Type: cross Abstract: Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel beh

arXiv:2609.33964v1 Announce Type: cross Abstract: Directional multi-transmitter localization from sparse received-power observations is difficult because the receiver observes only the source-unresolv

Automated lint: 17 errors, 59669 warnings, 3 info

arXiv:2609.29906v1 Announce Type: new Abstract: The estimation of Annual Average Daily Traffic (AADT) is vital for transportation planning and infrastructure maintenance, yet obtaining accurate values

arXiv:2609.27903v1 Announce Type: new Abstract: Due to the rapid development of information technology, a huge and complex traffic network has been established across various sectors, including aviati

arXiv:2609.27394v1 Announce Type: cross Abstract: Automotive spinning FMCW radar provides dense, 360^irc sensing and remains reliable under poor illumination and adverse weather, making it well-suited

arXiv:2609.27076v1 Announce Type: new Abstract: Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined percep

arXiv:2609.27442v1 Announce Type: new Abstract: 3D reconstruction from satellite imagery is essential for large-scale topographic analysis, yet the lack of high-fidelity training datasets with accurat

First: That the 7GB (int8) 2.1 comes even close to the 20GB (int8) 2511 is amazing, but maybe because of the smaller size some capabilities got lost. For some tasks 2.1 it is much better than 2511, bu

arXiv:2609.22375v1 Announce Type: cross Abstract: Introduction. Open collaborative knowledge graphs such as Wikidata increasingly ground agentic artificial intelligence, information retrieval, and lan

Automated lint: 14 errors, 54840 warnings, 3 info

arXiv:2601.08464v2 Announce Type: replace-cross Abstract: Context-aware building massing is an important early-stage design task: given a site for buildings, a generated massing should not only fit th

arXiv:2609.19863v1 Announce Type: cross Abstract: Navigation world models plan by foresight, predicting the future that each candidate action sequence produces and selecting the best, rather than mapp

arXiv:2609.20321v1 Announce Type: new Abstract: Autonomous vehicles depend entirely on Simultaneous Localization and Mapping (SLAM) to navigate safely in unknown environments. However, relying on a si

arXiv:2609.19961v1 Announce Type: new Abstract: Networked low-altitude unmanned aerial vehicles (UAVs) need reliable and adaptive decision-making capabilities to operate under uncertain observations,

arXiv:2609.20578v1 Announce Type: cross Abstract: Connected-vehicle safety evaluations rely on coupled traffic and network simulations, but standard channel models ignore radio resource competition in

arXiv:2609.20310v1 Announce Type: new Abstract: Human activity traces record individuals' timestamped visits to points of interest and are essential for applications such as mobility prediction and ur

arXiv:2609.17731v1 Announce Type: new Abstract: High-resolution land use and land cover (LULC) products derived from Sentinel-2 imagery are widely used for environmental monitoring and land management

arXiv:2609.18219v1 Announce Type: cross Abstract: Quantum Reinforcement Learning (QRL) represents policies as variational quantum circuits (VQCs), making it attractive for combinatorial optimization s

arXiv:2609.17781v1 Announce Type: new Abstract: Soft robotic systems have long been theorized as ideal candidates for use in search and rescue operations; however, there have been significant barriers

arXiv:2609.17488v1 Announce Type: new Abstract: We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws. LimiX

arXiv:2609.16168v1 Announce Type: cross Abstract: Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive assets, a

arXiv:2609.17107v1 Announce Type: new Abstract: Generative AI promises natural language access to the massive numerical telemetry of data centers and Industry 4.0 installations, yet text-to-query and

arXiv:2609.16443v1 Announce Type: cross Abstract: State-of-the-art visual locomotion controllers are increasingly capable at handling complex visual environments, making evaluating their real-world pe

arXiv:2609.13878v1 Announce Type: new Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale mod

arXiv:2609.15484v1 Announce Type: new Abstract: We report on the continued development of CatchMonitor, resulting in a prototype computer vision system designed to automatically quantify discarded fis

arXiv:2609.14383v1 Announce Type: new Abstract: Real-time pedestrian detection in driving scenes is constrained by three coupled failure modes: tiny targets lose discriminative evidence, occlusion wea

arXiv:2609.15219v1 Announce Type: new Abstract: Founders face two linked decisions: whether to pursue an idea before founding, and which operating actions and capital partners fit afterward. We presen

arXiv:2609.13805v1 Announce Type: new Abstract: In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, statio

arXiv:2609.12173v1 Announce Type: new Abstract: Transportation agencies need pedestrian volume estimates across entire road networks to prioritize safety investments, yet manual counts are expensive a

arXiv:2603.02638v2 Announce Type: replace-cross Abstract: Machine learning for mobile network analysis, planning, and optimization is often limited by the lack of large, comprehensive real-world datas

Automated lint: 12 errors, 54839 warnings, 3 info

arXiv:2609.10589v1 Announce Type: new Abstract: Heteroscedastic forecasting, where a network estimates a scale parameter alongside a location parameter, is normally motivated by uncertainty quantifica

arXiv:2609.09565v1 Announce Type: new Abstract: Agentic graph learning (AGL) has recently achieved promising results on graph reasoning tasks, where an agent powered by a large language model (LLM) se

arXiv:2609.11168v1 Announce Type: new Abstract: Tensor singular value decomposition (T-SVD), which is built upon the tensor-tensor product (t-product), has emerged as a powerful tool for processing hi

arXiv:2609.10139v1 Announce Type: new Abstract: Driver gaze provides information regarding driver visual attention and situational awareness to the surrounding traffic. Existing driver gaze estimation

arXiv:2609.08338v1 Announce Type: new Abstract: Object detection and tracking are fundamental components of perception systems for autonomous driving. Achieving robust performance under adverse condit

arXiv:2512.03936v2 Announce Type: replace Abstract: Autonomous driving planning systems perform nearly perfectly in routine scenarios using lightweight, rule-based methods but still struggle in dense

arXiv:2603.25565v2 Announce Type: replace Abstract: Current Large Multimodal Models (LMMs) in Earth observation are predominantly evaluated on planar optical tasks and often neglect the vertical dimen