arXiv subjects

Jiabo Liu

Publications and source records attributed to Jiabo Liu.

6 recordsLinked to original sources

arXiv2026

CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment

Geospatial representation learning from satellite imagery is a fundamental problem for large-scale urban analysis and real-world applications. Despite recent advances, current methods struggle with cross-region generalization and semantic interpretability due to their reliance on region-specific auxiliary data and the neglect of semantic alignment within multi-temporal urban imagery. Therefore, we present CoST, a novel \underline{Co}ntrastive-based \underline{S}patial-\underline{T}emporal framework that aligns spatial context with multi-temporal semantics to extract universal geographic regularities shared across regions. Specifically, CoST explicitly models spatial correlations to capture transferable geographic structures and exploits multi-year urban change semantics to align learned representations with high-level geo-semantics. Extensive experiments demonstrate that CoST consistently achieves superior performance across various downstream tasks and in unseen scenario, yielding an average relative gain of 8.7\% over the strongest competing methods across eight city-indicator settings. The code is available in \href{https://github.com/Arandinglv/CoST}{this repo}.

cs.CV↗

arXiv2026

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring. Existing methods typically formulate this task as multimodal representation learning, fusing heterogeneous urban data, e.g., satellite imagery, points of interest, textual descriptions, and 3D building information, into latent embeddings for prediction. However, these approaches are largely correlation-driven, assume cross-modal consistency, and rely on static pipelines, which limit their robustness in heterogeneous or unseen urban regions. We propose UrbanAgent, an agentic framework that reframes urban region profiling as a reasoning-driven inference problem. UrbanAgent instantiates an independent agent for each data modality and performs structured multi-agent collaborative reasoning to explicitly address cross-modal inconsistencies rather than absorbing them into a single representation. In addition, UrbanAgent extends indicator prediction as a closed-loop process of active evidence acquisition and iterative reasoning, enabling agents to verify uncertain inferences through tool-augmented retrieval of external knowledge optimized via reinforcement learning. Extensive experiments on global urban datasets for Carbon emissions, GDP, and Population estimation show that UrbanAgent consistently outperforms existing baselines, achieving an average improvement of 8.1% in R2, and exhibiting strong generalization performance in unseen-city settings.

cs.AI↗

arXiv2025

High-Performance KV$_3$Sb$_5$/WSe$_2$ van der Waals Photodetectors

Kagome metals AV$_3$Sb$_5$ (A = K, Rb, Cs) have recently emerged as a promising platform for exploring correlated and topological quantum states, yet their potential for optoelectronic applications remains largely unexplored. Here, we report high-performance photodetectors based on van der Waals KV$_3$Sb$_5$/WSe$_2$ heterojunctions. A high-quality Schottky interface readily forms between KV$_3$Sb$_5$ and WSe$_2$, enabling efficient separation and transport of photoinduced carriers. Under 520 nm illumination, the device achieves an open-circuit voltage up to 0.6 V, a responsivity of 809 mA/W, and a fast response time of 18.3 us. This work demonstrates the promising optoelectronic applications of Kagome metals and highlights the potential of KV$_3$Sb$_5$-based van der Waals heterostructures for high-performance photodetection.

cond-mat.mtrl-sci↗

arXiv2025

Asymmetric quantum Hall effect and diminished $\nu=0$ longitudinal resistance in graphene/InSe heterostructures

We investigate quantum transport in graphene/InSe heterostructures and find major asymmetries in the longitudinal resistance ($R_{xx}$) and vanishing $R_{xx}$ peaks at high magnetic fields, particularly at the charge-neutrality point. Our Landauer-Buttiker analysis and numerical simulations show that a monotonically varying density gradient combined with a full equilibration mechanism can explain these phenomena. Our results also suggest the presence of trivial long-range chiral edge current and offer a broadly applicable way to engineer transport properties in quantum Hall systems.

cond-mat.mes-hall↗

arXiv2025

CLIPPan: Adapting CLIP as A Supervisor for Unsupervised Pansharpening

Despite remarkable advancements in supervised pansharpening neural networks, these methods face domain adaptation challenges of resolution due to the intrinsic disparity between simulated reduced-resolution training data and real-world full-resolution scenarios.To bridge this gap, we propose an unsupervised pansharpening framework, CLIPPan, that enables model training at full resolution directly by taking CLIP, a visual-language model, as a supervisor. However, directly applying CLIP to supervise pansharpening remains challenging due to its inherent bias toward natural images and limited understanding of pansharpening tasks. Therefore, we first introduce a lightweight fine-tuning pipeline that adapts CLIP to recognize low-resolution multispectral, panchromatic, and high-resolution multispectral images, as well as to understand the pansharpening process. Then, building on the adapted CLIP, we formulate a novel \textit{loss integrating semantic language constraints}, which aligns image-level fusion transitions with protocol-aligned textual prompts (e.g., Wald's or Khan's descriptions), thus enabling CLIPPan to use language as a powerful supervisory signal and guide fusion learning without ground truth. Extensive experiments demonstrate that CLIPPan consistently improves spectral and spatial fidelity across various pansharpening backbones on real-world datasets, setting a new state of the art for unsupervised full-resolution pansharpening.

eess.IV↗

arXiv2025

Geospatial Representation Learning: A Survey from Deep Learning to The LLM Era

The ability to transform location-centric geospatial data into meaningful computational representations has become fundamental to modern spatial analysis and decision-making. Geospatial Representation Learning (GRL), the process of automatically extracting latent structures and semantic patterns from geographic data, is undergoing a profound transformation through two successive technological revolutions: the deep learning breakthrough and the emerging large language model (LLM) paradigm. While deep neural networks (DNNs) have demonstrated remarkable success in automated feature extraction from structured and semi-structured geospatial data (e.g., satellite imagery, GPS trajectories), the recent integration of LLMs introduces transformative capabilities for cross-modal geospatial reasoning and unstructured geo-textual data processing. This survey presents a comprehensive review of geospatial representation learning across both technological eras, organizing them into a structured taxonomy based on the complete pipeline comprising: (1) data perspective, (2) methodological perspective, and (3) application perspective. We also highlight current advancements, discuss existing limitations, and propose potential future research directions in the LLM and foundation model era. This work offers a thorough exploration of the field and provides a roadmap for further innovation in GRL. The summary of the up-to-date paper list can be found in https://github.com/CityMind-Lab/Awesome-Geospatial-Representation-Learning and will undergo continuous updates.

cs.CV↗