As an important data resource containing spatial information, addresses record the geospatial information corresponding to social production activities and human behavioral activities. How to effectively encode addresses has always been a core challenge in the field of Geographic Information Systems (GIS). Pre-trained Models (PTMs) designed for Natural Language Process (NLP) have emerged as the dominant tools for encoding semantic information in text. Though promising, those NLP-based PTMs fall short of encoding geographic knowledge in addresses, which limits their application potential in geospatial tasks. To tackle the above problem, this study proposes a Geography-Graph Pre-trained model (G2PTL) that combines graph learning and text pre-training, aiming to make up for the shortcomings of traditional PTM in the geography field. Specifically, we first utilize real-world delivery data to build a large-scale heterogeneous graph of addresses, which contains abundant geographic knowledge and spatial topology information. Then, G2PTL is pre-trained with subgraphs sampled from the heterogeneous graph. Through experimental evaluation on multiple downstream tasks of GIS, including geocoding, geographic entity prediction, and geographic entity recognition, G2PTL demonstrated significant performance improvements. G2PTL has been successfully deployed in production-level GIS, such as Cainiao's logistics system, effectively improving the execution efficiency and accuracy of address-related tasks. This research not only provides a new technical path for the encoding and processing of geographical information, but also opens up a new perspective for the study of pre-training models in the geographical field. The code resources of the G2PTL model have been opened for research and application developers to access and use at https://huggingface.co/Cainiao-AI/G2PTL.

The query-POI matching task involves interpreting noisy textual addresses to retrieve corresponding Points-of-Interest (POIs), which is crucial for location-based service providers. However, existing methods typically rely on annotated user search logs, limiting their generalization. This paper address the query-POI matching problem through geographical data alignment using a contrastive learning framework. Our model, MoCo-GA (Momentum Contrastive Geographical Alignment), learns similar representations for various geographical data elements of the same POI, including query address, POI address, and geolocation. We developed a method to create a cross-modal geographical dataset from crowd-sourced data for training. Our MoCo-GA employs the momentum contrastive instance discrimination mechanism to learn representations for textual addresses. We further proposed a siamese contrastive learning module to to for geographical data alignment. Experimental results demonstrate that MoCo-GA can consistently outperform baseline methods on query-POI matching task, particularly in zero-shot scenarios. Our code is available at https://github.com/CainiaoTechAi/TextGeoAlign

Real-world last-mile express datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile express dataset exists to support research in this field. In this paper, we introduce LaDe, the first publicly available last-mile express dataset with millions of packages from the industry. LaDe has three unique characteristics: (1)Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (2)Comprehensive information. It offers original package information, task-event information, as well as couriers' detailed trajecotries and road networks. (3)Diversity. The dataset includes data from various scenarios, including package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. We verify LaDe on three tasks by running several classical baseline models per task. We believe that the large-scale, comprehensive, diverse feature of LaDe can offer unparalleled opportunities to researchers in the supply chain community, data mining community, and beyond. The dataset and code is publicly available at https://huggingface.co/datasets/Cainiao-AI/LaDe.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains information on tumor morphology and physiology for breast cancer diagnosis and treatment. However, this technology requires contrast agent injection with more acquisition time than other parametric images, such as T2-weighted imaging (T2WI). Current image synthesis methods attempt to map the image data from one domain to another, whereas it is challenging or even infeasible to map the images with one sequence into images with multiple sequences. Here, we propose a new approach of cross-parametric generative adversarial network (GAN)-based feature synthesis (CPGANFS) to generate discriminative DCE-MRI features from T2WI with applications in breast cancer diagnosis. The proposed approach decodes the T2W images into latent cross-parameter features to reconstruct the DCE-MRI and T2WI features by balancing the information shared between the two. A Wasserstein GAN with a gradient penalty is employed to differentiate the T2WI-generated features from ground-truth features extracted from DCE-MRI. The synthesized DCE-MRI feature-based model achieved significantly (p = 0.036) higher prediction performance (AUC = 0.866) in breast cancer diagnosis than that based on T2WI (AUC = 0.815). Visualization of the model shows that our CPGANFS method enhances the predictive power by levitating attention to the lesion and the surrounding parenchyma areas, which is driven by the interparametric information learned from T2WI and DCE-MRI. Our proposed CPGANFS provides a framework for cross-parametric MR image feature generation from a single-sequence image guided by an information-rich, time-series image with kinetic information. Extensive experimental results demonstrate its effectiveness with high interpretability and improved performance in breast cancer diagnosis.