MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale
Authors: Ya Wen
Jixuan Cai
Qiyao Ma
Linyan Li
Xinhuan Chen
Chris Webster
Yulun Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: GeoAI
spatial representation learning
location embedding
multi-modal
contrastive learning
Primary area: other topics in machine learning (i.e., none of the above)
TL;DR: We present MoRA, a human-centric geospatial framework that leverages a mobility graph as its core backbone to fuse various data modalities, aiming to learn embeddings that represent the socio-economic context and functional role of a location.

Abstract: Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence, with increasingly diverging philosophies and techniques. While Earth observation paradigms excel at depicting locations in their physical states, we propose that a location’s full characterization requires grounding in both its physical attributes and its internal human activity pattern, the latter being particularly crucial for understanding its human-centric functions. We present MoRA, a human-centric geospatial framework that leverages a mobility graph as its core backbone to fuse various data modalities, aiming to learn embeddings that represent the socio-economic context and functional role of a location. MoRA achieves this through the integration of spatial tokenization, GNNs, and asymmetric contrastive learning to align 100M+ POIs, massive remote sensing imagery, and structured demographic statistics with a billion-edge mobility graph, ensuring the three auxiliary modalities are interpreted through the lens of fundamental human dynamics. To rigorously evaluate the effectiveness of MoRA, we construct a benchmark dataset composed of 9 downstream prediction tasks across social and economic domains. Experiments show that MoRA, with four input modalities and a compact 128-dimensional representation space, achieves superior predictive performances than state-of-the-art models by an average of 12.9\%. Echoing LLM scaling laws, we further demonstrate the scaling behavior in geospatial representation learning. We open-source code and pretrained models at: https://github.com/ylzhouchris/MoRA.

Published as a conference paper at ICLR 2026
MORA: MOBILITY AS THE BACKBONE FOR GEOSPA- TIAL REPRESENTATION LEARNING AT SCALE
Ya Wen'; Jixuan Cai Qiyao Ma', Linyan Li³, Xinhuan Chen', Chris Webster', Yulun Zhou
'Tee University of Hong Kong
WeChat, Tencent
CCity University of Hong Kong
werya@ccnnect... hku. hk, eeryyccaiggaatie com, gyma @omm@connect.... hku.hk,
Linyanliecityu. edw.ey...eu...... hk, chenxinhuancen gmail. i com,
cwebster@hku. hk, yulunzhouthku..
ABSTRACT
Representation learning geospatial locations remains a core challenge in achiev- ing general geospatial intelligence, with increasingly diverging philosophies and techniques. While Earth observation paradigms excel at depicting locations their physical states, we propose that a location's 's full characterization requires grounding in both its physical attributes and its internal human activity pattern, the latter being particularly crucial for understanding its human-centric functions. We present MoRA, a human-centric geospatial framework that leverages human mobility graph as its backbone fuse various data modalities, aiming learn embeddings that represent the nocio-economic context and functional role of location. MoRA achieves this through the integration of spatial tokenization, GNNs, and asymmetric contrastive learning to align 100M+ POTs, massive remote sensing imagery, and structured demographic statistics with billion-edge mobility graph, ensuring the three auxiliary modalities are interpreted through the lens of fundamental human dynamics. To rigorously evaluate the effectiveness of MoRA, we construct benchmark dataset composed of 9 downstream prediction tasks across social and economic domains. Experiments show that MoRA, with four input modalities and compact 128-dimensional representation space, achieves superior predictive performances than state-of-the-art models by an average 12.9%. Echoing LLM scaling laws, further demonstrate the scaling behavior in geospatial representation learning. We open-source code and pretrained models at: https//ggttbbboom/////ooomnooott
INTRODUCTION
At the heart of Geospatial Artificial Intelligence (GeoAl) lies fundamental challenge: the translation of geographic location-a complex, multifaceted entity defined by physical features, human activity, and latent relationships-iito dense, low-dimensional vector that machine can process and reason with (Chen et al., 2025] Zhang et al.) 2024; Mai et al. 2025 Such a vector, or "representation" enables models to understand spatial patterns, predict socio-economic outcomes, and track dynamic changes. However, the path toward creating these representations has split into two dominant paradigms, each attempting to answer different fundamental questions about the nature of place. This divergence shapes every subsequent decision, from data acquisition and model architecture to the very definition of what a location embedding should represent.
The first major paradigm centers on Earth observation. It seeks to describe the "physical" state of locations and build consistent digital twin of the planet's surface. Such digital representations provide richer geographic priors than raw coordinates, offering location context that improves downstream tasks such as fine- fine-grained image classification(Mar Aodha et al., 2019 Cole et al. 2023). A standout example within this paradigm is Google's AlphaEarth (Brown et al. (225), which ingests petabytes of raw sensor data from a multitude of sources, i.c., optical satellites, radar, LiDAR,
"Equal contribution. Work done when Ya Wen was an intern at Tencent.
1 "ooresponniig author.

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Published as a conference paper at ICLR 2026
and regularizes this heterogeneous information into unified representation. Other works learns embeddings by aligning coordinates with visual imagery that delineates the physical appearance of locations, either geo-tagged photos satellite imagery, using contrastive learning (Vivanco Cepeda et al. 2023 Mai et al. 2023a Klemmer et al., 2025).
The human-centric paradigm stands out, in sharp contrast, as the second major stream, built on the premise that a location's 's meaning is defined more by the intricate patterns of human activity that unfold within it. Existing studies have employed various types of human-related data, such human mobility data (Zhou et al. 2023; Yong & Zhou 2024) and demographics (Wen & Zhou 2024). to model
dynamics and predict outcomes including housing prices, crime patterns, and consumption levels (Xu & Zhou| [2024] Zhang et al., (022). However, developing an effective approach, especially grounded by robust and principled rationale, to utilizing inherently multimodal geospatial data remains significant and unresolved challenge. There is also lack of comprehensive benchmarks for human-centric tasks, making it difficult to evaluate the inference capabilities of location representations.
To address these challenges, we claim that the true, latent meaning of a location is primarily defined by its functional relationships with other locations, revealed through the patterns of human movement, instead of its intrinsic attributes. The theoretical justification is explicitly drawn from an analogy to the functioning of Large Language Models (LLMs), where the semantic meaning of word (a "token"" is not inherent but emerges from its context- the other words with which it frequently co-occurs in vast text corpora. Similarly, inspired by Vision Transformers (ViT), we discretize continuous geographic space into cells, treating these discrete grid cells as "spatial tokens" and human mobility sequeccs of movements from one cell to another-as the "sentences" that provide context. A cell's representation is thus profoundly enriched by the latent co-occurrence structures revealed in the sequence- human movement patterns.
To this end, we propose MoRA, a framework that leverages mobility as the backbone for geospatial representation learning at scale. By using a human mobility graph as the central structural backbone for multimodal data alignment, MoRA ensures that all other data modalities are interpreted through the lens of these fundamental human dynamics. Our contributions can be summarized as follows:
We emphasize mobility's 's fundamental role the "syntax of geospatial space", which gives functional meaning to region tokens by structuring them in sequences, and propose MoRA, framework that uses solid relational backbone (mobility) enriched by functional (POIs), social (demographics), and physical (imagery) attributes to learn uniquely comprehensive, humen-centric location representations.
Drawing a parallel to the large-scale training paradigms in LLMs, we apply and validate MoRA using a massive mobility graph with extensive long-range links, yielding high-quality, transferable region representations at the national scale. We also provide, to our knowledge, the first empirical evidence of scaling laws in geospatial representation learning.
We construct benchmark evaluation dataset of downstream socio-econone economic predic- tion tasks. Extensive experiments show that our model achieves significantly superior iefformance-improviin by 12.9% on average- across all tasks.
We open-source our methodology, benchmark dataset for human-centric representation evaluation, and a distilled version of MoRA presented as a simple, peeeccypresrrving. ready-to-use utility (coordinates in, vectors out) for national-scale geospatial inference.
2 RELATED WORKS
Representation of the physical states of locations. Several studies encode the natural and built environmental features of locations into machine-interpretablle representations, enabling analysis of natural phenomena and monitoring of environmental changes (Brown et al., 2025 Klemmer ct al. 2025). Such approaches often rely on geo-tagged and satellite images, or more broadly on Earth observation data (Yin et al. 2019 2021 CSP (Mai et al. 2023a) learns embeddings from unlabeled imagery with a contrastive objective, but is pretrained, fine-tuned, and tested on the same datasets (iNat2018 (Van Horn et al., 2018) and fMoW (Christie et al.) (018)). ), without demonstrating generalizability to other tasks. SatCLIP (Klemmer et ad.][025 and GeoCLIP (Vivanco Cepeda
et al. 2023) both train location encoders via contrastive learning coordinate-image pairs, differing
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mainly in encoder architectures and pretraining data. Despite showing success in specific tasks such as worldwide image gco-localization, such visual models may miss crucial non-visual context.
Pretrained foundation model for human-centric geospatial inference. Geospatial data is in- herently diverse and multimodal, spanning maps (Balsebre et al., 2024
Choudhury et al. (025).
texts (Li al. 20222] 2023 Feng et al. 2025). images (Fan et 2023 Huang al. 2023), and graphs (Wu et al. 2022] Wang & Lij 2017). all widely employed to advance the understanding of urban areas. To leverage this diversity, multimodal region representation learning has emerged paradigm that integrates cross modal information in supe-supe manner to learn task-agnostic
regional embeddings Yan al.) 2024 Zhang al. 2020 2022: Yong & Zhou 2024). However, such models are often trained separately for each city and show substantial performance drop when trans- ferred across
of sensitivity domain shifts and of gaps in learning city-agnostic geospatial priors. Following the success of pretrained Foundation Models (FMs) in language and vision domains, there is increasing interest in foundation models for geospatial artificial intelligence
(GeoAl) (Mai al. 2024 Zhang al. 2024). ReFound (Xiao et al. (024) is early attempt that distills knowledge from general FMs while adding comain-specific objectives, but its pretraining data, i... POIs and satellite image data from five major Chinese cities, are limited in scope and modality diversity compared with ours. Conceptually closest to our work is the Population Dynamics Foundation Model (PDFM) (Agarwal et al., 2024), which encodes postal codes and counties across the United States by leveraging human activity signals such as regional busyness and search trends. Despite advances in scope, still falls short in modality richness and diversity. More importantly, fuses modalities through simple concatenation and aggregates information based solely on a static neighborhood graph defined by geographic proximity. This approach misses the dynamic, non-local, and higher-level correlations between regions.
Position encoding functions. A straightforward way to represent geolocations is to analytically map 2D/3D coordinates into higher-dimensiooall vectors that preserve geographical distances. Space2 Vec MMai et al.] 2020) encodes locations using multi-frequency sinusoid functions a 2D Euclidean space. Neural Radiance Fields (NeRF) (Mildenhall et al., 2021) encodes continuous 3D coordinates via Fourier input mapping. To model Earth's s curvature, Sphere2 Vee (Mai et al., 2023b) and Siren (Rubwurm al. 2024) use position encoders that capture spherical distances between coordinates. These learning-free free position encoders are typically followed by neural network, trained in a supervised manner on task-specific data.
METHODOLOGY
The framework learns versatile region representations by combining spatial tokenization, Graph Neural Networks (GNNs), and asymmetric CLIP- contrastive learning. It adopts a mobility- as-backbone architecture that aligns diverse geospatial modalities to mobility anchor: Points of Interest (POIs) as texts, satellite imagery as visual data, and demographic distributions as tabular categorical histograms. Figure] T illustrates the proposed methodological framework.
3.1 MOBILITY AS THE BACKBONE
Geographic location provides precise anchor for aligning multimodal data. Prior region embedding methods (Wu et al., 2024 Klemmer et al. 20255) often fuse modalities via coordinates, focusing solely on imagery, which limits the incorporation of relational signals. Human activity routinely spans geographic boundaries via transport or digital networks, and mobility data- rep-esenreede links in a spatial graph captures these non-local patterns. We propose mobility graph as the backbone for multimodal fusion and alignment geospatial representation learning.
Tokenizing Locations for Graph. Raw spatial interactions are sparse and misaligned in time and space, making coordinate-based graphs too fragmented to reflect major flows. Inspired by Vision Transformers (ViT) (Khan et al. 2022). which partition images into patches, we grid geographic space and build graphs at the cell level. We adopt the H3 grid (Uber Technologies, Inc.). which minimizes distortion compared Geohash and Google S2 (Jiag & Zhou 2024), offering balanced global coverage. At level (~33 km per cell), we construct a nationwide mobility graph from real-world transaction data as presented in the "Graph Construction" section in Figure]
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Node Info
For
POl-txt
Salelito-lmage
Demographicy
HS grid
Coordinate
B] Unl-modal Encoding
Text Encoder (BGE-m3)
Encoder (aaooaCCl
Demo Ereicder
Off line Payments
Downstream
Graph Structure
Spatial Network
Mobility
Social
Backbone
Grid Tokenizer (H3)
Economic
Natural
Mr
A] Node Embedding
B) Uni-modal Encoding
Mobility Greph Grid
c) Multimodal CLIP
Graph Construction
Multimodal Pretraining
Inference
Figure 1: The methodological framework of MoRA.
Pre-encoding. We utilize LINE (Large-scale Information Network Embedding) (Tng al. 2015 to preprocess the full mobility graph and generate 128-dimensional embeddings for each H3 cell. We select LINE embeddings with second-order proximity due their superior performance in empirical evaluation. Paired with the graph sampling strategy described below, this approach effectively preserves full-graph information with minimal computation.
Graph-based mobility encoder. Graph Neural Networks (GNNs) have proven to be effective for capturing relationships between nodes, taking into account high-order dependencies. GNNs operate through message-passingg mechanism, where each node updates its representation by aggregating information from neighbors. To encode graph & using L layers of GNNs, the 7th layer embedding of node is computed as follows:
AGG (((-1) 1c M (ENN)
where Nú refers to the neighborhood of the node i, and AGG(-) is the aggregation function, which vary across different models. We use LightGCN (He et al. 2020) the mobility graph encoder. simplifies the traditional GNN architecture by removing feature transformation and nonlinear activation, and only keeping the most essential component: neighborhood aggregation. In LighIGCN, node features are initialized as learnable parameters, and L layers of propagation yield + 1 embeddings per node, namely (c("), e()) (()))
(+1) Σ
(2)
V.
JEN,
The final embeddings are computed by summing all layer embeddings:
K e,
(3)
k=
We initialize node features with pre-encoded LINE embeddings and feed a mobility subgraph into the encoder. This subgraph is extracted from the full graph using a top-k sampling strategy based on percentage threshold, ensuring that the links with the highest flows are retained for each node. This strategy aims to address the "long-tail" distribution patterns commonly seen in geospatial studies, where small number of regions experience very high flows, while the majority of regions exhibit significantly lower flows that have minimal impact on the overall graph structure. We adopt top sampling ratio of 10% for the main experiments.
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where the three assisting modalities are aligned with mobility individually and simultancously by Published as a conference paper at ICLR 2026
3.2 AUXILIARY MULTIMODALITY ENCODER
Different unimodal encoders are selected to encode auxiliary modalities. (1) print-of-Intereee (POI): We encode both POI categorical statistics and textual information with the BGE-m3 language model (Chen et al.) 2024] Cheng et al.) 2025), averaging all POI embeddings within each H3 cell to obtain the grid embedding. (2) Satellite imagery: We use an off-the-shelf pretrained RemoteClip (Liu et al. 2024) encoder to produce grid-level image embeddings, (3) Demographic Statisties: Categorical demographic tabular data is encoded with an MLP-based encoder.
3.3 MUL.TIMODALITY ALIGNMENT WITH MOBILITY
We train with CLIP-inspine contrastive learning scheme: all four modalities are projected into shared space, with mobility as the anchor modality to which the others are aligned. We construct modality tuples (M, I, T, D) for all grids in the study area, where M indicates mobility, and I, T, D represent images, texts, and distribution data, respectively. Given the mobility feature m, of a grid and its corresponding observations in other modalities I, t; d; we encode them into normalized embeddings by modality-specific encoders: f(m:) E IR and g(x)) E E where x E (I, t, d}. graph neural network, whereas g are pretrained foundation models for texts and images (see Section 3.2), and an MLP for demographic categorical features. The encoders are optimized with the objective
Specifically,
L [l,t,d
E (LM,x + Lx.M) XE(I.td)
(4)
N
(o)((((m)..gx)))))))) Σj=1 exp((s(mi)) (xx)))/r)
L = 2N
log
(5)
which maximizes the similarity between a grid's mobility features and its corresponding POI, image, or demographic features, while minimizing similarity to those of other grids in the same batch (m,, x=) and a symmetric loss Lx.M doing the other way around. (;--) represents the dot product operation and r is a temperature parameter adjusting the smoothness of the logits in the softmax.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
4.1.1 MULTIMODAL PRETRAINING
Mobility dataset. We construct spatial network using mobility flows derived from Tencent's electronic payment ecosystem, nationwide transaction platform in China with extensive offline coverage. Each offline store is mapped to an H3 grid cell. By linking cells based on shared weekly transactional traffic across different grids, form weekly mobility graph. Aggregating 54 weeks across 50 million stores yields a large-scale graph at H3 resolution level 6, with approximately 200,000 nodes and 1.2 billion edges.
Auxiliary modality datasets. We leverage 100M+ Points-of-Interest (POIs) from Tencent Maps, each with name and category label spanning 445 categories (Tencent LBS Platform 2024). POI names are typically short, referring to store names, company names, types of public facilities. For imagery, we fetch Google Satellite imagery at approximately 10m resolution (zoom level 14) for each H3 grid. Demographic statistics are derived from WorldPop (Bondarenko et al. 2025) by aggregating population count per H3 grid, stratified by 10-year age bands, gender, and work-home status in 2020.
Implementation details. We split the dataset into 90% for training and 10% for validation to mitigate overfitting. The model is trained for 100 epochs with a batch size of 20,480, using the Adam optimizer with learning rate of 0.001 and weight decay of 0.001 single NVIDIA A100 GPU for
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Table 1: Summary of datasets for downstream tasks.
Abbreviation
Deseription
Duta Source
Sputial Scule
Sample number
Publicly Available
Social Tacks
POP
Population density
WorldPop [Bondrenko et al 2025
Grid
194,232
Y
EDU
Average education years
Census Year Baok
2120)
County/Disriie
2,800
Y
ELD
Elderly (65+) pogulation ratio
Census Year Book
NBS
2100
CountyDistrict
2,800
Y
HSR
Hukon separation ratio
Census Year Book
NBS
2020
County/Distriet
2,800
Y
Crime cases
Zhang et
39,932
Economic Tosks
Nighttime light intensity
MOSAIKS Rolf 2021 Census Year Book NES 2020
HOU
Per capita housing area
CountyDDisrrie
2,800
Y
ENE
Energy consumption
statistical Tenceut
City/Prefecture
283
N
Offline consumption amount
Grid
194,052
one hour. During training, we conduct grid search over commonly used hyperparameters: learning rate E (le-4, 2, 5e le-3, 2e 2e-3, 5e - and weight decay {le-3, le 2, 1e- The LINE embeddings for the nationwide mobility graph were computed Tencent's Angel distributed graph computing platform (Jiang et al. 2018), utilizing hundreds computing cores. Graph nodes with more than billion edges were embedded efficiently within few hours.
4.1.2 DOWNSTREAM EVALUATION
Downstream tasks and datasets. To assess model's generalizbility, curated set of downstream tasks as shown in Table [ These tasks were selected for their diversity and broad representativenesss, spanning the two key domains of human-centric scenarios: social and economic. Considering the influence of statistical unit delineation in geospatial analysis, the downstream tasks designed span four distinct spatial scales point, grid, county/district, and city/prefecture to examine resolution effects in region representation learning. The samples are evenly distributed across the study area, ensuring an evaluation free of geographic bias, Detailed descriptions and spatial distributions of the dataset samples are provided in Appendix A.4 MoRA is trained at the H3 grid level. For point-level tasks, individual point values within each grid are aggregated to align with the grid-level embeddings. For administrative-Iess each unit and use the resulting mean for regression. This multi-scale evaluation allows us to assess the model's generalizability across varying spatial scales.
tasks, we average embeddings over all grids within
Prediction models. We implement LightGBM (Ke et al. 2017) as the downstream prediction model. The hyperparameters runs is reported along with the standard deviation. To enhance performance while mitigating the risk of overfitting, different sets of hyperparameters variation in sample sizes. For city-level electricity consumption prediction tasks, Ridge regression is adopted due to the limited number of data samples.
lightly tuned, and the average performance the test set 10
used for tasks at different levels, considering the
4.2 PERFORMANCE COMPARISON
Baseline models.
We benchmark MoRA against the strong existing approaches from two categories:
Approaches with publicly available, pre-trained embeddings, which we evaluate directly our benchmark tasks and datasets. These can be further grouped into: 1) Self-supervised pretraining
approaches: AlphaEarth (Brown al. 2025), SatCLIP (with ViT-16) (Klemmer et al. (025).
GeoCLIP (Vivanco Cepeda et al. 2023), and CSP (Mai et al. 2023a): and 2) analytical position- encoding functions: Sphere Vec (sphereC+) (Mai et al. (023b). Siren (RuBwurm al. 2024). and NeRF (Mildenhall et al.) (021). 1. (The selection is based methods superior performance reported previous work (Wu et al. 2024)). Note that MoRA operates on H3 grids as its spatial unit. To ensure consistency, for the location encoders used for comparison that take 2D coordinates as input, we generate location embeddings for the centroids of the H3 grids for comparison with our grid embeddings. For the pixel-level encoder, i.e., AlphaEarth, the mean of all pixel embeddings within each grid is computed as the baseline. We use grids covering the entirety China for comparison.

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Published as a conference paper at ICLR 2026
Table 2: Comparison of model performances on downstream tasks spanning social and economic aspects. Previous best results are underlined, while best results are highlighted in bold. Model performances are measured by R², and the standard deviations over ten repeated trainings are marked in brackets below. Row A illustrates the percentage difference between Our Method's performance and the best baseline.
Social Tasks
Economic Tasks
Model
Dim.
POP
EDU
ELD
HSR
CRI
NTL
HOU
ENE
COS
State the--tee-eeeee art pretra rat ned
Locat on Encoders
AlphaEarth
0.80
0.77
0.71
0.68
0.71
0.63
0.63
0.47
0.81
(0.02)
(0.01)
(0.02)
(0.03)
(0.01)
(0.02)
(0.03)
(0.02)
(0.01)
SatCLIP
256
0.52
0.63
0.68
0.74
0.39
0.33
0.66
-0.07
0.44
(0.03)
(0.01)
(0.03)
(0.03)
(0.01)
(0.03)
(0.03)
(0.11)
(0.03)
GeoCLIP
512
0.41
0.66
0.66
0.69
0.32
0.24
0.65
0.11
0.32
(0.04)
(0.01)
(0.02)
(0.03)
(0.01)
(0.03)
(0.02)
(0.01)
(0.00)
CSP
256
0.55
0.65
0.62
0.68
0.39
0.29
0.62
0.20
0.46
(0.03)
(0.02)
(0.03)
(0.03)
(0.01)
(0.03)
(0.02)
(0.03)
(0.03)
Sphere2Vec
96
0.40
0.56
0.62
0.61
0.34
0.26
0.57
40.01
0.35
(0.03)
(0.02)
(0.04)
(0.03)
(0.01)
(0.03)
(0.02)
(0.01)
(0.03)
NeRF
96
0.40
0,63
0.61
0.61
0.33
0.24
0.56
0.03
0.33
(0.03)
(0.02)
(0.03)
(0.03)
(0.01)
(0.03)
(0.02)
(0.05)
(0.02)
Siren
1,024
0.51
0,66
0.69
0.74
0.39
0.33
0.66
-0.14
0.44
(0.03)
(0.02)
(0.03)
(0.03)
(0.01)
(0.03)
(0.03)
(0.14)
(0.03)
Ours
MoRA
0.83
0.85
0.81
0.81
0,76
0.62
0.70
0.72
0.91
(0.02)
(0.01)
(0.02)
(0.02)
(0.00)
(0.02)
(0.02)
(0.02)
(0.01)
4.1%%
+ 10.5%
13.9%
9.5%
6.9%
-1.7%
+6.1%
+54.5%
+12.1%
2. Approaches that not directly applicable, requiring further work to generate embeddings, either by training published model on our dataset or by sourcing compatible input data to perform inference with their pre-trained weights. Since most existing models from this type were designed and tested at the city scale instead of a national one, we implement all models in Jiangsu Province for fairer comparison (see Figure 2 for Jiangsu's 's location). We select HREP (Zhou et al. 2023). ReCP (Li et al.) (024), and ReFound (Xiao et al. (0224) as representative works from each type as baselines. See Appendix A.5 for details of the baselines and our implementation for comparison.
Comparison results. In Table we compare MoRA's predictive performance against pre-trained embeddings across range of tasks and report results by downstream task category. Clearly, MoRA delivers superior performance across all tasks, averaging a 12.9% improvement overall, with 10.8% and 16.0% gains for the social and economic tasks, respectively. Given that human-centrr tasks hold great value in practice and typically do not exhibit easily inferable location-dependent properties, an average R2 of 0.78 across nine diverse tasks demonstrates that MoRA effectively captures complex, high-level regional correlations beyond simple geographic proximity. delivering strong practical value. Note that the very slight underperformance on nighttime- prediction (1.7%) likely stems from implicit correlations and co-variation between common Earth observation features and the target indicator. We further evaluated MoRA's performance in predicting urban carbon emissions, task spanning natural and economic domains (Appendix A.2). MoRA achieved a superior performance than AlphaEarth, demonstrating that the mobility backbone captures correlations between intensive human activity and environmental externalities that physical-only encoders might overlook.
The superiority of MoRA consistent across varied spatial scales, achieving the best performance on tasks at city, county, and grid levels. Notably, in the cityllevel energy consumption task, MoRA demonstrates impressive results with a prediction accuracy of 0.72 in terms of R In contrast, the baseline models exhibit R² values mostly fluctuating around zero, while approach exceeds the second-best by 54.5%, revealing their limited capacity to capture regional features at coarser scales.
For the second category of approaches, Table shows that MoRA, with compact 22-dimensiionl representation, consistently outperforms both baselines, featuring an average gain of 20.9% over the second-best. This underscores MoRA's methodological advantages as generalizable framework.
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Table 3: Performance comparison between MoRA and methodological baselines in Jiangsu
Province (R²). The A row reports the percentage improvement over the strongest baseline.
Social Tasks
Economic
Tasks
Model
Dim
POP
EDU
HSR
NIL
HOU
ENE
COS
HREP
128
0.57
0.55
0.24
0.49
0.68
0.34
0.42
0.38
0.73
ReCP
96
0.33
0.64
ReFound
768
0.41
0.71
0.45
0.51
0.73
0.47
0.35
0.80
0.68
MoRA
128
0.68
0.75
0.74
0.53
0.73
0.85
18.0%
4.1%%
22.4%
+77.2%
+ 1.4%
16.9%
+ 25.7%
-8.4%
+ 15.6%
Inference. For practical deployment, we develop and open-source a distilled version of MoRA that captures core geospatial knowledge and enables direct, coordinate-only inference. Specifically, train a multi-layer perceptron (MLP) to map continuous geographic coordinates to high-dimensional embeddings, supervised by MoRA's grid-level outputs. This allows practitioners to generate high- quality, privacy-preserving embeddings at arbitrary locations using only geographic coordinates, completely eliminating the need for external datasets and H3-based lookups inference time. Detailed implementation of the distillation approach is provided in Appendix A.I
4.3 SCALING BEHAVIOR IN GEOSPATIAL REPRESENTATION LEARNING
Echoing scaling behaviors observed in LLMs (Kaplan et al. 2020 we examine how model perfor- mance in geospatial representation learning scales with pretraining data size and spatial coverage. Figure[ishowe model performances of three MoRA variants trained on three nested spatial coverages, namely Jiangsu, East China, and China. The spatial extents shown in vigure[ir) We conducted three separate experiments with different pretraining data sizes where Jiangsu contains 3,904 H3 cells, East China contains 28,855 cells and China contains 195,574 cells. All three pretrained models are tested in Jiangsu only, using evaluation methods described in Section
We observe that downstream performance generally increases with larger pretraining data for both social and economic tasks (Figure 2 Appendix A..3). 'hee performance gain from Jiangsu to East China is especially large, while the increase from East China to China relatively marginal. External training information can be useful for internal predictions. The marginal benefits of increasing training data size gradually decline. The scaling behavior in geospatial representation learning consistent with findings in language and vision domains, where model performance improves with scale (Henighan et al. 2020). Results emphasize the value and necessity of geospatial representation learning scale. Even for small-scale predictions, increase in training data size and spatial coverage will introduce relevant information and improve downstream predictive performances.
Social
Economic Tasks
0.9
COS
EDU
CRI EME
2 0.7
NTL
0.5
J5LBS5 11 colls
HOU
East China
Province
10f
10a
10*
10"
Number
Number Colls Training
Figure 2: Scaling behavior of downstream task performance with pretraining data size and spatial coverage. Map on the right illustrates the spatial extent of Jiangsu Province, East China, and the entire China, respectively covering 3,904, 28,855, and 195,574 H3 cells used for the pretraining. Left figure illustrates average Ri values for all tasks, social tasks, and economic tasks respectively, while the middle figure illustrates task-specific scaling behaviors. Detailed numbers in Appendix A.3
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4.4
ABLATION AND SENSITIVITY STUDY
(b) Anchoniing ablation
(b) Module-wise
Sampling Senattivity
1.00
1.00
Mith (rst
Hola Same. 19%
All Libe ootiee
beme: 27h
anctered
list mwckeer WolkoWeggoreeee graph)
Samp.
Randory Zamp.
2
0.63
0.60
0.59
0.50
0.50 Sodal
Figure 3: Model comparison results for ablation studies and sensitivity analysis.
Ablation study on anchoring modality. We frame mobility as the backbone for aligning other modalities and justify this design theoretically. Here, we empirically evaluate its unique advantages over other anchoring choices. As shown in Figure 3a., mobility-as-bcckae backbbe significantly outper- forms alternative anchor modalities across both task types, achieving an average of 16.8% gain over the second-best (the cenoo-nchored structure), validating its superiority.
Module-wise ablation study. Figure 3)) shows the performance of multiple model variants on downstream tasks. The columns labeled w/o CLIP indicate that removing the CLIP-based alignment module and leaving only the mobility modality leads to average performance drop of 6.5%. Second, mobility forms the backbone of MoRA, with graph information injected at two stages. We assess each by ablating them separately. Removing the graph encoder and substituting a simple MI.P (w/o Mob Graph Encoder) causes the largest drop, indicating the GCN captures non-local patterns via relational structure. Replacing LINE initialization with random values (w/o LINE Initialization) has smaller impact, suggesting that the graph structure is more informative than pre-encoded embeddings. However, consistent 1-2% decline shows that LINE still provides complementary value. We further replace the mobility graph with simple neighborhood graph in which edges connect only directly adjacent cells MoRA(Geighhort) 12.2% drop in average performance, highlighting the mobility graph's 's ability to capture long-range spatial correlations, i.e., "functional adjacency", that absent in graphs based solely direct neighborhood adjacency.
graph)). This leads to
Ablation on auxiliary modalities. We ablate each of the three auxiliary modalities, i.e., POTs, satellite imagery, and demographics, to assess their contributions the model performance on human- centric inference. As shown in Table the removal of deomographics information consistenly results in the largest performance decrease across nearly all tasks, demonstrating its unique advantages in capturinng human-related features, in line with the findings of Wen & Zhou (2024). Images and POIs offer essential additional information, boosting performance across both types of tasks. Overall, MoRA achieves the best performance in every domain and downstream task, validating the effectiveness of its multimodal fusion approach.
Table Ablation auxiliary modalities in nobility-as-backbon fusion (1t2).
Social Tasks
Economic Tasks
AVG R²
POP
EDU
ELD
HSR
CRI
NTL
HOU
ENE
COS
w/o POI
0.835
0.835
0.796
0.800
0.755
0.590
0.702
0.680
0.895
0.765
w/o Image
0.832
0.841
0.791
0.803
0.755
0.599
0.673
0.665
0.900
0.762
w/o Demo
0.824
0.827
0.765
0.793
0.753
0.605
0.685
0.590
0.893
0.748
MoRA
0.829
0.853
0.812
0.810
0.759
0.615
0.703
0.719
0.909
0.779

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Sensitivity to graph sampling. Given the mobility graph encoder's importance, analyze sensitivity to neighbor sampling strategy and ratio (Figure We compare our top-k sampling approach with random sampling, which retains links randomly based a percentage threshold instead of depending on link weights. The results suggest that top-k sampling shows minimal performance variation across ratios (top 10% top 30%). Random sampling, by tending to include nodes from the "long-tail", introduces noise and generally underperforms top-k at the same ratios. As result, random sampling generally underperforms top-k sampling at equivalent ratios. Yet as the random ratio increases, more generalized patterns offset the noise, leading to improved performance. Overall, MoRA balances accuracy and efficiency across graph configurations.
Sensitivity to H3 grid resolution. We extend our study at H3 resolution level 6 to its adjacent levels, 5 and 7, to evaluate the model's performance across different spatial scales. Compared to level 6 H3 cells, which cover an average area of 36.13 km?, cells at levels 5 and 7 cover approximately 253.61 km² and 5.18 km², respectively. The East China region is selected for testing to facilitate ease of operation without losing generalizability. We reconstruct the training dataset for levels 5 and from scratch, including forming trajectory-based mobility graphs, gathering satellite imagery and POIS for encodings, and processing demographic distributions data. Three separate experiments conducted, with corresponding sets of downstream data processed for evaluation. Since cities in East China are limited in quantity, we choose Ridge Regression for energy consumption prediction across all levels to ensure consistency. For the remaining tasks, LightGBM is adopted. Table 5 reports model performance with varying spatial resolutions. Overall, performance generally improves as spatial granularity increases, though the rationale varies by the level of the downstream tasks. For grid-level predictions, finer resolution can enhance localized prediction. However, an exception is observed with crime prediction, which follows reversed pattern. This might be explained by the fact that crime becomes more heterogeneous and exhibits greater variations at finer scales. At the county and city levels, where aggregation smooths out local noise and highlights more significant features, performance improves with grid granularity.
Table 5: Sensitivity of model performance using different grid resolutions (R*).
Grid-level
County-level
City-level
NTL
cOs
CRI
POP
EDU
ELD
HOU
HSR
ELE
H3 lv5
0.462
0.881
0.838
0.679
0.645
0.668
0.611
0.830
0.659
H3 lv6
0.632
0.901
0.764
0.817
0.790
0.698
0.665
0.835
0.668
H3 lv7
0.741
0.934
0.634
0.836
0.821
0.754
0.700
0.872
0.711
5 CONCLUSION AND DISCUSSION
This work introduces MoRA, a mobility- thtebb---o--e-- framework for geospatial representation learning. Centered around billion-scale mobility graph and enriched with multimodal alignment (including POIs, remote sensing imagery, and tabular data), MoRA learns unified region embeddings that generalize across diverse downstream tasks. Extensive experiments on 9 socio-economic tasks demonstrate MoRA's consistent outperformance of state-of-the-art methods. Moreover, our findings echo the scaling laws observed in other domains: expanding training data's spatial coverage from local to national scales significantly enhances both representation quality and task performance.
Despite strong empirical results, limitations remain. The scale issue in spatial representation learning is not fully resolved, despite our sensitivity analysis indicating the robustness of MoRA across spatial scales. Second, although MoRA itself as a framework is dataset-agnostic and can be adapted to alternative, publicly available movement data sources (e.g.. Internet data mobile phone data), high-quality human mobility data may be difficult to obtain in some regions. In such cases, we suggest neighborhood-basee variant of model viable and effective alternative that produces high-quality geospatial representation.
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ACKNOWLEDGEMENTS
This work was supported by National Key R&D Program of China (2022YB3300500), The authors would like thank Wei Qi for providing the China Statistical Yearbook data and administrative boundary data.
ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics, Recognizing the privacy considerations associated with human mobility data, rigorously minimize associated risks through four-stage anony minztiionion- eegregatoonllarrinngdiiiilllil
pipeline:
1. Anonymization: The mobility data is derived from the locations of offline stores. Grids are linked based observed mobility flows between stores in different regions within week, and these links are aggregated into an inter-grid network. No individual-leve information is retained.
2. Aggregation: Raw mobility data aggregated at a much coarser grid level, ensuring that no precise geographic coordinates recorded.
3. Representation Learning: Embeddings inherently generalize patterns.
4. Model Distillation: We share distilled model, ensuring that reconstructing the original data or tracing back specific individuals is practically impossible.
This aligns with established privacy Sawvacy-resseving practices in mobility research. The disclosed embed- dings contain no personally identifiable information, and our distillation adds an additional layer of critical protection.
REPRODUCIBILIIY STATEMENT
We have made every effort to ensure high level of reproducibility of our work. Specifically, we:
including data prepro- cessing pipelines, model architecture, and code for downstream task evaluation.
Release the complete codebase at
Describe the training implementation in detail in Section 4.1 including training steps, hardware details, model hyperparameters and so on.
Make the pretrained model weights available by open-souring high-performance distilled version of our model, enabling researchers to immediately benefit from MoRA's capabilities via simple, direct queries.
Establish and release benehmark downstream dataset, accompanied by detailed descriptions of task properties and visualizations of their geographical distributions in Appendix A.4

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A APPENDIX
A.1 DETAILED IMPLEMENTATION OF THE DISTILLATION
For model distillation, fit surrogate Multi-Layer Perceptron (MLP) network estimate pretrained region embeddings from MoRA. For each H3 cell, the input for the MLP is a 1024-d vector, which is SIREN encoding (Rußwurm al. 2024) of the centroid coordinate of the cell. The output for the MLP is 128-d vector from MoRA. The MLP is configured with 8 hidden layers, with dimension of 512 each. The model is trained with MSE loss function minimize the deviation of estimated embeddings from our true embeddings. The learning rate is set to 0.005 for 5000 epochs. Final training loss is 0.0857. The downstream performance of the distilled model is shown in Table Note that the performance of the distilled model (MoRA,)) tends to improve as the number of MLP MLPlayers and the size of the hidden dimensions increase. We here demonstrate the distillation effectiveness preliminarily using the model size mentioned above.
Table 6: Performance comparison between MoRA, and baselines.
Social Tasks
Economic Tasks
POP
EDU
ELD
HSR
CRI
NTL
HOU
ENE
COS
SatCLIP
0.523
0.633
0.676
0.735
0.391
0.334
0.655
-0.069
0.443
MoRA
0.829
0.853
0.812
0.810
0.759
0.615
0.703
0.719
0.909
MoRAa
0.730
0.835
0.804
0.816
0.580
0.517
0.693
0.742
0.729
A.2 GENERALIZATION TO URBAN CARBON EMISSION PREDICTION
While MoRA is primarily optimized for socio-economic contexts, its utility extends to environmental domains where human activity serves as primary driver. We evaluated MoRA against AlphaEarth in predicting nationwide temporl urban carbon emissions (2020-202) using high-resolution satellite-derived dataset (Fan et 2025). To account for the temporal dynamics of emissions, we augmented the static 128-dimensional representations with two cyclical features: time of year and time of day. urban areas, MoRA RR2 0.612) outperforms AlphaFarth (R² 0.605). This result suggests that mobility- -anchore backbone effectively captures the intensive human activity patterns, such as road traffic, that dictate urban emission externalities, which surface-level physical encoders may partially overlook.
A.3 RESULTS TABLE FOR THE SCALING BEHAVIOR
Table 7 reports task-wise performance for the scaling experiments described in Section 4.3
which
also serves the source data for Figure
Table 7: Downstream task performance (R2) under model variants in scaling behavior.
Jiangsu
East China
China
Social
POP
0.675
0.746
0.755
EDU
0.753
0.840
0,817
ELD
0,551
0.724
0.748
HSR
0.809
0.883
0.858
CRI
0.743
0.748
0.750
Economic
NTL
0.547
0.551
0.616
HOU
0.529
0.648
0.581
ENE
0.733
0.810
0.889
COS
0.846
0.861
0.891
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Consumption
Crime
County-level tasks
Nightlights
Population
City-level energy consum
Figure 4: The geographical distribution of various downstream dataset samples.
A.4 DETAILS ON DOWNSTREAM DATASETS AND SPATIAL DISTRIBUTION
The data sources summarized in Table are primarily drawn from WorldPop (Bondarenko al. 2020), MOSAIKS (Rolf et al., 2021 and the 2020 China Census Year Book from the National Bureau of Statistics of China (NBS) 2020). Additionally, ENE (energy consumption) is compiled from various local statistical yearbooks. The total energy consumption of one city/prefecture is computed by converting different forms energy usage into tonnes standard coal equivalent. We also include an unavailable- wwwaaillblle-otthee toeelle-tttheepppeee the-public dataset, COS (offline consumption amount) from Tencent, evaluate whether the embeddings can predict regional consumption indices. For the crime dataset, using the original data from (Zhang et al. 2025), we aggregate cases over five-year interval, add I and apply a log transform to the labels.
Figure deppccss the geographical distribution of the downstream task datasets. Overall, samples are widely spread across China. Note, however, that the west contains vast plateaus and deserts (c.g.. the Tibetan Plateau) with minimal human activity, leading to sparse coverage for several tasks. In regions with active human presence, samples are distributed fairly evenly. Importantly, these maps reflect only the lask-specffic data availability- collection processes and independent of our region embeddings, which are uniformly distributed across populated areas of China.
sparsity stems from the nature of the tasks their
A.5 BASELINES
We benchmark our framework against types of baselines: (i) feature-based extractors that enable direct, coordinate-based. training-free embedding queries through pretraining or analytical transformation functions, and (ii) methods that require either retraining or the preparation matching input dataset. Since the first category aligns closerly with our approach and is covered in details in Related Works, the discussion here focuses on the second category. notable line of work in this category adopts graph-based multi-view representation learning: for each view (e.g., mobility, POI), a graph is constructed based on attribute similarity, and the resulting node embeddings are fused (e.g., via attention mechanisms) to produce unified region representations. Contrastive-base methods- mostll formulated between two views or modalities the data- are also widely used for scale region representation learning. Besides, there early attempts for building region representation fundation model through multi-city pretraining or distillation from LLMs and VLMs. Accordingly, select three representative state-of-the-as works from each category as baselines:
strictly
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Published as a conference paper at ICLR 2026
HREP constructs heterogeneous graph from three types of regional data, namely, human mobility, POIs and geographic neighboring relationship, and learns region embeddings using relation-aware GCN and attention based cross-view fusion.
ReCP leverages views from POI and human mobility data, applies contrastive learning at both intra-view inter-view levels enhance the cross-view consistency, and finally fuse the two views via concatenation.
ReFound integrates multi-source grospatial data through transformer- based structure and distills knowledge from language, visual foundation models joinlt to enhance generaliza- tion capabilities. The model is pretrained in five major cities in China to acquire general geospsatial knowledge.
The baselines for HREP and ReCP require re-training, whereas ReFound provides pre-trained weights for inference. However, because the datasets for both input features and downstream evaluation are unavailable for all three models, we adapt their codebases and released weights to our datasets in the same study area. Specifically, we find and align required data with each model's 's input format, re-train HREP and ReCP, and run inference with ReFound.
A.6 THE USE OF LARGE LANGUAGE MODELS (LLMS)
We employed large language model (LLM) exclusively for language polishing, including grammar correction, stylistic refinement, and improvements to clarity and readability. The LLM did not contribute to the conception of the study, data collection, model design, statistical analysis, interpretation results, nor did it generate figures, tables, or substantive content. All methodological choices, analyses, and conclusions were conceived, executed, and validated by the authors. Drafts edited with the LLM were subsequently reviewed line by line by the authors to ensure technical accuracy, fidelity the original meaning, and alignment with ethical and scholarly standards. The authors bear full responsibility for all scientific content presented in this work.
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Reviews:
[{"review_id": "0ixgM2yUug", "reviewer": "Reviewer_5Ex7", "summary": "The paper proposes a graph-based location representation learning framework that is based on relative location transitions from one (hexagonal) grid cell to another. The framework, despite architecturally complex, is well justified and experimentally strong across spcial and economic tasks. While I would also appreciate benchmarking on natural downstream tasks (mentioned in Figure 1, but not benchmarked against), I understand that the premise of mobility mainly works on social and economical processes. \n\nOverall, I find the paper well-written and well-presented and the results convincing.", "questions": "* With MORA, would it be possible to pre-compute and store embeddings for all H3 cells in a region — similar to how AlphaEarth maintains a precomputed embedding database?\n* At inference time, does one need to supply POI/satellite imagery or demographics to obtain an embedding, or can the trained GNN alone generate embeddings without requiring additional data downloads?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Clear and well-justified framework for graph-based location representation learning via relative transitions across hexagonal grid cells.\n* Strong empirical performance on spatial and socioeconomic downstream tasks.\n* Well-written and clearly presented.", "weaknesses": "* Fairly complex methodology including single-modality encoders and a separate graph neural network. A joint unifying architecture would be less engineering-focused. However, ablations justify the individual components well\n* It would have been nice to also capturing natural tasks, where mobility matters. For instance, species distribution modelling, like iNaturalist of BirdSnap would be good choices here", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2ElcLFhZSi", "reviewer": "Reviewer_Ay9J", "summary": "### Problem\n\nThe authors tackle the problem geospatial intelligence. Specifically, the seek to convert locations into useful vector embeddings to support tasks such as predicting socio-economic indicators or urban dynamics. They argue existing approaches fall into two silos:\n\t1.\tEarth-observation-centric: learning from satellite imagery and physical features.\n\t2.\tHuman-activity-centric: learning from mobility and demographic patterns.\n\nThese paradigms rarely integrate well, and current models miss long-range functional relationships between places (e.g., commuter flows) and lack scalable benchmarks. The field also lacks foundation-style models trained at national or global scale. The authors argue that (2) Human-centric representations are fundamental the semantics of place and are a first class citizen in their solution\n\n\n### Solution\n\nThe authors propose MoRA, a framework that leverages mobility as the backbone for geospatial representation learning at scale. Their method has the following properties:\n\t•\tAggregates real mobility flows across millions of locations and billions of edges.\n\t•\tBuilds a nationwide mobility graph using H3 spatial grids.\n\t•\tLearns human-centric location embeddings with a graph neural network \n\t•\tAligns mobility with three auxiliary modalities using asymmetric CLIP-style contrastive learning.\n\t•\tDemonstrates scaling laws in geospatial representation learning.\n\t•\tReleases a benchmark of 9 socio-economic tasks and a distilled privacy-preserving model.\n\nMoRA consistently outperforms state-of-the-art representations across prediction tasks.", "questions": "- My main question is about generalization outside of China? Can the authors demonstrate that MoRA can perform well in other national geographies?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Strong empirical results. Mora outperforms all baselines across several experiments\n- Ablation studies validates the additional complexity of their method\n- Experiments are done across several training runs, informing reproducibility. Means and standard deviations are provided. \n- The introduction of the new geospatial benchmark is noteworthy\n- The experiments around scaling laws are useful for the GeoAI community", "weaknesses": "- Heavy reliance on proprietary mobility data: \n    - This causes concerns related to reproducibility. I don't believe this resource is publicly available\n    - What about rural areas where human activity is sparse?\n- Single country mobility data: The mobility data is unique to china, casting fairly large doubts on the generalizability of the method. For example, does this method generalize to Europe or the US?\n- The new benchmark primarily describes human-centric socio-economic tasks. It would make sense that mobility data would be more useful here (as demonstrated in their experiments). Other tasks such as land cover classification would likely see better results under the more Earth-observation-centric representation learning strategy. Unfortunately, this trade-off is left unexplored.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Nd8JFngQuB", "reviewer": "Reviewer_adgb", "summary": "SUMMARY: The authors propose a new geospatial location representation learning framework, leveraging multimodal input data, namely imagery, POI data and demographic information (the latter two available as text). The most unique aspect about this work is that instead of leveraging raw geographic context (e.g. pure coordinates, as used in SatCLIP or GeoCLIP), the authors build a spatial graph between locations based on whether locations are associated with each other in the WeChat Payment system. While this obviously assumes a lot of prior knowledge, it is an interesting approach to explore specifically for geo representation learning in urban areas, where we can assume such data to be available. This gives their whole approach a strongly informed \"geographic prior\". The authors test their geo embeddings on a set of different socio-economic tasks, showing impressive performance and outperforming recent embeddings such as GeoCLIP or AlphaEarth.", "questions": "Overall this is an interesting, well motivated paper. I like both the methodological innovations and the experiments, though there could be more details and an important ablation is missing. To help with my understanding and the final assessment, I would ask the authors to address my concerns and questions outlined in the \"Weaknesses\" sections.\n\nReferences:\n\n[1] Rolf, Esther, et al. \"Mission Critical--Satellite Data is a Distinct Modality in Machine Learning.\" arXiv preprint arXiv:2402.01444 (2024).\n[2] Mac Aodha, Oisin, Elijah Cole, and Pietro Perona. \"Presence-only geographical priors for fine-grained image classification.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "STRENGTHS:\n\n- I like the framing of two paradigms for geo representation learning, the human and EO centric ones. Not sure I necessarily agree but this is an interesting framing to motivate the paper! The argument about the relative nature of a \"place\" (i.e. a location being majorly defined by its relationship with other locations) is thought provoking and interesting. Overall, really great motivation section!\n\n- Exploring scaling laws for geospatial representation learning is a critical research challenge, e.g. also mentioned here [1]. The authors do that a little bit (though I would have liked to see more of that).\n\n- I like that the authors clearly define distinctions from PDFM, which is conceptually closest.", "weaknesses": "- I would love to have seen a comparison to a simple neighborhood graph; the way understand it, edges between cells/nodes are based on a-priori known WeChat Pay interactions. This is great if you have access, but what if you didn't have that data? How would this method perform if you simply built your graph based on direct neighborhood/adjacency of cells? How would the model perform then? This seems like a crucial ablation missing, unless I am missing it. This also would allow the authors to compare their approach to PDFM, which is only available for the US - and which would be an important comparison.\n\n- In the abstract, the authors say that \"While Earth observation paradigms excel at depicting locations in their physical states, we claim that a location’s comprehensive “meaning” is better grounded in its internal human activity pattern\". I don't want to start a philosophical discussion about \"place\", but this sentence feels a bit strong / definitive. Human activity might give a location \"meaning\" relevant to some applications - especially human centric ones, but might be irrelevant for other, e.g. natural processes.\n\n- Given that your paper basically argues for a geographic prior for SSL geo-representations (in your case based on payment data), I think it would be important to link your work back to the origins of work on \"geographic priors\", especially [2].\n\n- I don't like averaging over R2 values of different tasks in Tab 2; this does not seem very rigorous and I'd recommend to remove that column.\n\n- Fig 2: sizes of legend circles don't match the circle sizes in the plot", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lWDdDGhqJX", "reviewer": "Reviewer_L9Si", "summary": "MoRA presents a human-centric geospatial representation paradigm that uses billion-scale mobility graphs as the backbone: it encodes H3 grid cells into 128-D vectors with GNNs, aligns satellite imagery, POIs and demographics via contrastive learning, boosts downstream socio-economic tasks by 12.9% on average, demonstrates the first scaling laws in GeoAI, and releases a privacy-preserving distilled model that maps coordinates directly to high-quality embeddings.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "MoRA leverages a billion-edge mobility graph as its backbone, aligns imagery, POIs and demographics into a unified 128-D space via contrastive learning, delivers a 12.9% average gain across nine nationwide socio-economic tasks, demonstrates the first scaling laws in GeoAI, and releases a privacy-preserving distilled model that infers embeddings from coordinates alone.", "weaknesses": "1. The design of downstream evaluation does not align with main stream methodology.\nWhy utilize LightGBM as the downstream prediction model instead of a linear probing method that is employed by many existing works [1,2,3,4]\n\n2. The coverage of dataset is limited. MoRA only utilized dataset from China. How to ensure the generalization of the induced conclusion?\nIt is suggested to compare more cities in othe counties.\n\n\n3. The technique contribution is limited. Actually, combining mobility with POI and Satellite images is a common practice in region representation learning.\nWhat MoRA has done is move this paradigm into location embedding, while the requirement of multiple different kinds of data modality also diminish the generalization and practicability. That is why previous location embedding work [1,2,3] applied global application while this work only limited on China data.\n\n4. I am confused of the inference practice. The paper states that during inference, it still needs 3 kinds of data (POI, demographic, satellite images) for prediction. If so, what is the meaning of contrastive learning in pretraining stage? The CLIP-based contrastive learning should inject the information of POI, image, demographics data modality into mobility backbone through contrastive alignment in pretraining. In inference, the common practice is to only utilize the pretrained mobility backbone.\n\n---\n[1] Klemmer, Konstantin, et al. \"Satclip: Global, general-purpose location embeddings with satellite imagery.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 4. 2025.\n\n[2] Hao, Xixuan, et al. \"Nature makes no leaps: Building continuous location embeddings with satellite imagery from the web.\" Proceedings of the ACM on Web Conference 2025. 2025.\n\n[3] Vivanco Cepeda, Vicente, Gaurav Kumar Nayak, and Mubarak Shah. \"Geoclip: Clip-inspired alignment between locations and images for effective worldwide geo-localization.\" Advances in Neural Information Processing Systems 36 (2023): 8690-8701.\n\n[4] Hao, Xixuan, et al. \"Urbanvlp: Multi-granularity vision-language pretraining for urban socioeconomic indicator prediction.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 27. 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)