Organizations: Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, and College of Urban and Environmental Sciences, Central China Normal University, Wuhan, 430000, HuBei, China · School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430000, HuBei, China
Dual-source encrypted points of interest (DSEP), POIs from two encrypted coordinate systems, suffer from intertwined location and attribute uncertainties, including nonlinear systematic misalignment and naming inconsistency, hindering land-use/land-cover (LULC) mapping. To the best of our knowledge, this paper is the first to propose an LLM-driven, training-free location-attribute synergic closed-loop optimization paradigm for DSEP fusion. The paradigm jointly refines location transformation and attribute correspondences through iterative feedback. Attribute-synergic location fusion uses an LLM-driven attribute matching method to establish DSEP correspondences, reducing matching complexity from O(N^2) to O(N), and refines transformation coefficients using an improved particle swarm optimization algorithm within ISODATA-clustered local subregions. Location-synergic attribute fusion then reassesses attribute confidence from updated geometric residuals through an LLM-fuzzy method. The refined correspondences feed back into location optimization, forming a bidirectional closed loop. Sample purification and adaptive radius contraction enable convergence in essentially two iterations. We further propose a training-free LULC mapping method that inherits land-use classes from encrypted maps through location fusion, producing vector-raster integrated LULC maps. A reference-free POI fusion evaluation method is applied across 31 provincial capitals and municipalities in mainland China. Experiments show that our method achieves an average DSEP location fusion residual of 4.58 m and attribute fusion accuracy of 95.12%, improving upon the open-source baseline and state-of-the-art method by 1.77 m and 14.87%, respectively. Overall, the method provides a training-free solution for DSEP fusion and enables georeferencing of encrypted vector data to WGS-84 without field-surveyed ground control points.
Figures & tables
Figure 1: Illustration of coordinate offset and attribute inconsistency in dual-source encrypted points of interest (DSEP) fusion. Although POI attributes or locations are different in multiple sources, location and attribute are interdependent for the same geographic entity and can synergically support each other in the DSEP fusion.
Attribute of Location
Baidu Maps
Amap
Name
北京工业大学附属中学(双桥分校)
北京工大附中双桥分校
English Name
Beijing University of Technology Affiliated High School (Shuangqiao Branch)
Affiliated High School of BJUT, Shuangqiao Branch
Address
双桥中路32号院
双桥中路32号院
English Address
No. 32 Courtyard, Shuangqiao Middle Road
No. 32 Courtyard, Shuangqiao Middle Road
Coordinate (longitude, latitude)
116.596075, 39.899951
116.589449, 39.894216
Table 1: Semantic differences in the representation of the same POI between Baidu Maps and Amap.
Table 2: Comparison with existing POI fusion approaches across key differential axes.
Figure 2: Research route. The steps of the algorithm are as follows: I.Global POI fusion begins by aligning textual attributes and coordinates to obtain an initial set of fused data. II. Local POI fusion is conducted by dividing the data into spatial subregions. Within each subregion, POI attributes are preliminarily fused using an LLM. III.The intelligent optimization algorithm further reduces location residuals. IV. Fuzzy mathematics is employed to resolve attribute ambiguities, resulting in the final fused POI dataset. The dashed loop between Attribute-synergic location fusion and Location-synergic attribute fusion indicates the iterative loop: attribute correspondences guide location refinement, and the refined geometry in turn re-conditions attribute confidence, forming a bidirectional feedback that progressively co-optimizes both stages until convergence.
Figure 3: The local POI matching algorithm based on the local search circle
Figure 4: The basic architecture of DeepSeek-Chat [ 7 ] , which is similar to the transformer block and consists of multiple blocks composed of multi-head latent attention (MLA), a DeepSeekMoE-based feedforward network, and root mean square layer normalization (RMSNorm).
Type of prompts
Content of prompts
Environment Prompts
In subsequent interactions, I will provide two formatted Chinese addresses, separated by an underscore. You are required to determine purely based on semantics whether the two addresses refer to the same location and respond with a confidence score between 0 and 1 (where 0 indicates they are definitely not the same address and 1 indicates they are definitely the same address).
Judgment Rules
Determine whether the addresses refer to the same location based on the following principles: If a larger-scale location contains a smaller-scale location, they should be considered different locations. For instance, "Beijing International Studies University" and "Beijing International Studies University-Qinxue Building" should be identified as different locations. If the descriptions differ but the addresses are semantically identical, they should be recognized as the same address. For example, "No. 1, Lane 3, Dong’an Street" and "No. one, Third Lane, Dong’an Street, Fengtai Town" should be considered the same address. Please note that I will provide multiple groups of addresses (different groups will be separated by "|"). Please respond in the same sequence and format accordingly.
Manually set the content of the dialog 1
Q : "Beijing International Studies University""Beijing International Studies University"|"Building 307, Yard 28, Guangqu Road""Building 307, Yard 28, Guangqu Road"
Table 3: Predefined prompts. By incorporating environment prompts, judgment rules, and predefined multi-round dialogues, the LLM produces structured outputs, enabling efficient aggregation of multi-source POI attribute matches.
Similarity
Levels
Similarity
Levels
1>sA,ij≥0.9
I
0.9>sA,ij≥0.8
II
0.8>sA,ij≥0.6
III
0.6>sA,ij≥0.4
IV
0.4>sA,ij≥0.1
V
0.1>sA,ij≥0
VI
Table 4: Uncertainty levels of POIs attribute fusion. Levels I: Exactly the same, II: Very similar, III: Basically similar, IV: Uncertain, V: Dissimilar, and VI: Completely inconsistent.
Figure 5: Study areas. The basemap is based on approval No. GS (2024) 0650 reprojected into an equidistant cylindrical projection to ensure consistent latitude and longitude intervals.
Figure 6: Illustration of residual coordinate (geometric) errors in X/Y (longitude and latitude) direction. Due to space limitations, subfigures (a), (b), (c), and (d) show residuals for representative cities located in the north, east, south, and west of China (Harbin, Shanghai, Haikou, and Urumqi), respectively, where the randomness of residuals can be found in different directions.
LLMs
Algorithm
Performance Metrics (m)
Mean
Median
Origin
1286.74
1286.95
ChatGPT
FSR-PSO
888.67
941.54
PSOPoly
598.05
577.31
PSOTrig-OLS
904.68
964.64
GOLS
915.28
978.8
Table 5: Location registration error of particle swarm optimization (PSO) based on different objective functions. PSOPoly represents the objective function based on formula 13 , while FSR-PSO refers to the process of applying PSOPoly to the coordinates followed by OLS. The same applies to PSOTrig-OLS based on the objective function formula 15 .
Figure 7: Illustration of residual coordinate (geometric) errors in X/Y (longitude and latitude) direction after global-to-local location fusion (GTLLF) .
Method
Mean (m)
Std (m)
Median (m)
Origin
1286.74
2.94
1286.68
TPS
17.65
9.87
15.58
ETT
6.35
3.92
5.49
GTLLF (Ours)
4.58
2.82
3.91
Table 6: The comparison of residuals for different methods. Statistics were aggregated from per-city results. Detailed results for all cities are provided in Table S2.
Table 7: Ground truth validation of fusion accuracy using the synthetic encrypted dataset. For GTLLF, Iter-1, Iter-2 and Iter-3 correspond to the residuals after the first, second and third outer synergic iterations respectively, where Iter-3 is the reported final result. Bold values indicate the best performance among empirical trigonometric transformation (ETT), thin-plate spline (TPS) and global-to-local location fusion (GTLLF) for each LLM.
Figure 9: Attribute fusion evaluation results
Figure 10: Google Maps LULC data in Kunming, China. Within mainland China, Google Maps provides only basic categories and lacks fine-grained urban LULC elements for some cities, and its data updates are infrequent, limiting its applicability as a LULC reference.
Method ( N )
mIoU (%)
Macro-F1 (%)
LEPF-LULC (Ours, N =31)
75.14 ± 5.74
83.11 ± 4.90
EULUC ( N =26)
58.58 ± 6.83
72.01 ± 5.74
Table 8: Quantitative comparison of land-use/land-cover (LULC) classification accuracy between EULUC and the proposed joint LLM- and encrypted POI-driven training-free LULC mapping (LEPF-LULC). Metrics are mean Intersection-over-Union (mIoU) and Macro-F1, reported as mean ± standard deviation (%) across cities. N denotes the number of cities evaluated. EULUC results were computed over 26 cities where EULUC provides coverage; cities with no EULUC data were excluded from the mean calculation for fairness. Detailed results for all cities are provided in Table S3.
City
Direct Matching (times)
Fixed Distance Matching (times)
Local Search Circle (times)
Shanghai
46958
24.83
1.35 (94.56% ↓ )
Shenzhen
34069
32.58
1.34 (95.89% ↓ )
Guangzhou
34758
24.00
1.45 (93.96% ↓ )
Average
36202
27.14
1.38 (94.92% ↓ )
Table 9: Comparison of points of interest (POI) pairwise computations. The proposed local search circle significantly reduces computations, demonstrating much higher efficiency compared to exhaustive direct matching and fixed distance matching with a predefined search radius.
Location encoders map geographic coordinates to high-dimensional descriptors through contrastive alignment with paired Earth Observation imagery. We prove that this construction is structurally limited: any Lipschitz location encoder produces an output of intrinsic dimension at most 2, regardless of architecture or bandwidth, which forces the contrastive loss to strictly exceed its asymptotic optimum. Empirically, joint training also collapses the encoder's embedding covariance to low rank. Although dimension-limited, current location encoders sidestep this spectral collapse by training against a frozen image encoder, which we reinterpret as a covariance regularizer that supplies a high-rank target for the location encoder to fold onto. However, extending this approach to multimodal inputs is challenging, as location encoders should simultaneously map a single location to multiple, potentially incompatible, descriptors. We identify two requirements a regularizer must satisfy for location encoding in multimodal regimes: co-alignment across modalities, since adapters alone cannot align independently pretrained encoders; and high effective rank, since the location encoder inherits its target's spectrum. We propose LoCA (Location Contrastive Alignment), a two-stage pipeline that satisfies both. Stage 1 jointly trains Sentinel-1 and Sentinel-2 encoders with contrastive learning, producing a co-aligned, high-rank multimodal target. Stage 2 freezes the image encoders and trains a location and a tabular metadata encoder as projection adapters with multi-view contrastive learning. Against five state-of-the-art encoders on 17 tasks ranging from land cover and wildfire forecasting to geophysical, socio-economic, biodiversity and climate applications, LoCA achieves the most task wins, is the only model to outperform every other on a majority of tasks, and wins 68.8% of all per-task comparisons.
Geographic coordinates are common inputs in applied machine learning, but latitude and longitude describe position rather than the surrounding environment. Image-grounded location encoders learn this context from satellite or ground-level imagery; structured open map data offers another source of supervision whose utility for continuous coordinate encoders remains less explored. We introduce CartoVec, a coordinate encoder trained to predict Overture-derived land cover, land use, transportation, place and connectivity targets at multiple spatial scales, without image inputs or downstream-task labels. We evaluate frozen embeddings on Census outcomes, Airbnb prices and Philadelphia crime in the US Northeast using spatial block cross-validation. CartoVec has the highest mean performance on most Census outcomes and the highest average across the full suite, with several comparisons close across folds. The findings support open map data as a practical source of reusable location representations with a coordinate-to-vector interface that requires no map lookup at inference. The open-weight model is available at https://huggingface.co/gursoyfurkan/CartoVec.
Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR provides synchronized spatial-spectral information, and deep learning (DL) enables 3D point cloud semantic segmentation; however, adoption is limited by the lack of publicly available urban and suburban MS LiDAR datasets aligned with National Mapping and Cadastral Agencies (NMCAs) classification schemes. This study addresses these gaps by introducing L1 and L2 NMCA-aligned LULC classification schemes and a new benchmark MS LiDAR dataset. We evaluate seven state-of-the-art DL models and perform spectral ablation studies at both levels of detail. Results show that Point Transformer V3 achieves the best performance, with mIoU of 79.4% (L1, 8 classes) and 58.9% (L2, 20 classes) using a dual-wavelength LiDAR system (532 nm and 1064 nm). Ablation results show that multispectral information improves performance over geometry-only inputs, with gains of 1.1 percentage points at L1 and 7.8 points at L2. These results highlight the value of LiDAR reflectance for fine-grained material discrimination and support the evolution of NMCA LULC schemes toward higher semantic detail. The Loosdorf-MSL dataset contributes a new benchmark for consistent national and international LULC mapping.
Narges Takhtkeshha, Aldino Rizaldy, Markus Hollaus +3
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Trento, Italy · Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria · Helmholtz-ZentrumDresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF), Freiberg, Germany +2