activity
20242026
most citedSpatialLLM: From Multi-modality Data to Urban Spatial Intelligence

1 citations · 1 across the 9 of their papers we have counts for

collaborators

12 papers

cs.CV2026

Expert Knowledge-Guided Decision Calibration for Accurate Fine-Grained Tree Species Classification

Chen Long, Dian Chen, Ruifei Ding +3

Accurate fine-grained tree species classification is critical for forest inventory and biodiversity monitoring. Existing methods predominantly focus on designing complex architectu…

cs.CV2026

Unleashing the Capabilities of Large Vision-Language Models for Intelligent Perception of Roadside Infrastructure

Luxuan Fu, Chong Liu, Bisheng Yang +1

Automated perception of urban roadside infrastructure is crucial for smart city management, yet general-purpose models often struggle to capture the necessary fine-grained attribut…

cs.CV2026

SVII-3D: Advancing Roadside Infrastructure Inventory with Decimeter-level 3D Localization and Comprehension from Sparse Street Imagery

Chong Liu, Luxuan Fu, Yang Jia +2

The automated creation of digital twins and precise asset inventories is a critical task in smart city construction and facility lifecycle management. However, utilizing cost-effec…

cs.RO2025

Aerial-ground Cross-modal Localization: Dataset, Ground-truth, and Benchmark

Yandi Yang, Jianping Li, Youqi Liao +5

Accurate visual localization in dense urban environments poses a fundamental task in photogrammetry, geospatial information science, and robotics. While imagery is a low-cost and w…

cs.CV2025

LifelongPR: Lifelong point cloud place recognition based on sample replay and prompt learning

Xianghong Zou, Jianping Li, Zhe Chen +4

Point cloud place recognition (PCPR) determines the geo-location within a prebuilt map and plays a crucial role in geoscience and robotics applications such as autonomous driving,…

cs.CV20251 cited

SpatialLLM: From Multi-modality Data to Urban Spatial Intelligence

Jiabin Chen, Haiping Wang, Jinpeng Li +3

We propose SpatialLLM, a novel approach advancing spatial intelligence tasks in complex urban scenes. Unlike previous methods requiring geographic analysis tools or domain expertis…