Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models
Steffen Knoblauch, Hao Li, Gengchen Mai, Konstantin Klemmer, Song Gao, WenWen Li
Abstract
Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale. Recent advances in self-supervised learning have given rise to Earth Observation Foundation Models (EOFMs), which leverage petabyte-scale unlabeled EO data to learn transferable representations across a wide range of downstream geospatial tasks. Despite these advances, current EOFMs remain largely confined to raster modalities, overlooking the rich, structured information encoded in openly-accessible vector data sources such as OpenStreetMap and Overture. Vector data provides explicit and compact representations of geographic entities, including geometry, topology, and semantic relationships, offering critical contextual signals that are often ambiguous or inaccessible in imagery alone. Raster and vector data thus represent complementary views of geographic space: raster data captures continuous physical and spectral patterns, while vector data encodes discrete objects and their relational structure and often represents more of the human rather than the physical systems (e.g. social or demographic data). However, existing geospatial representation learning paradigms treat these modalities in isolation, relying on imperfect and often lossy transformations to bridge them. This perspective paper calls for a paradigm shift toward joint Spatial Representation Learning (SRL) in an unified embedding space that integrate raster perception with vector-based reasoning. Building on emerging efforts in multimodal geospatial learning, we highlight conceptual foundations, technical challenges, and promising directions for aligning heterogeneous spatial data sources. We contend that such integration is essential for developing next-generation geospatial AI systems capable of more accurate, interpretable, and semantically grounded understanding of the Earth.
AI Impact Assessments
(1 models)Scientific Impact Assessment
Core Contribution
This is a perspective paper (not an empirical contribution) that argues for a paradigm shift in geospatial foundation models: moving from raster-only representation learning to unified Spatial Representation Learning (SRL) that jointly embeds raster data (satellite/aerial imagery) and vector data (OpenStreetMap, Overture, POIs, building footprints, road networks, etc.) into shared representation spaces. The paper's main intellectual contribution is synthesizing the landscape of existing raster-based Earth Observation Foundation Models (EOFMs) and vector-based encoding approaches, articulating why their isolation is limiting, and proposing a research agenda for integration.
The paper identifies a genuine gap: current EOFMs (SkySense, SpectralGPT, SatCLIP, Prithvi, etc.) operate almost exclusively on raster modalities, while vector data—which encodes human-defined spatial structure, geometry, topology, and semantics—is treated separately or converted lossily into raster form. The authors argue that raster captures continuous physical/spectral patterns while vector encodes discrete human-organized spatial entities, making them fundamentally complementary.
Methodological Rigor
As a perspective paper, there are no experiments, formal proofs, or quantitative evaluations. The rigor must therefore be assessed based on the quality of argumentation, comprehensiveness of the literature review, and precision of the proposed research directions.
Strengths in argumentation: The paper clearly articulates why raster-to-vector and vector-to-raster conversions are lossy and non-differentiable, creating fundamental barriers to end-to-end learning. The identification of the Modifiable Areal Unit Problem (MAUP) as an inherent limitation of rasterization is well-placed. The four-pronged research agenda (methodological innovations, fairness, evaluation, uncertainty/interpretability) is reasonably structured.
Weaknesses in rigor: The paper remains largely at a high level of abstraction. The proposed "unified transformer backbone with bidirectional cross-modal attention" is described only conceptually without any architectural specifics, loss function formulations, or even pseudocode. The discussion of how to actually align a vector polygon to its corresponding pixel set—acknowledged as a key challenge—is not developed beyond stating the difficulty. The paper does not offer concrete baselines, ablation hypotheses, or experimental protocols that could validate the proposed paradigm. Some claims, such as that joint embeddings would "enable zero-shot reasoning over combined visual-structural evidence," are aspirational rather than grounded in evidence.
The literature review, while extensive (60 references), is somewhat surface-level in its treatment of individual works. The discussion of Rose, GeoLink, SkyScript, and S2Vec correctly identifies their limitations but doesn't deeply analyze *why* their architectural choices were made or what specific technical barriers prevented fuller integration.
Potential Impact
The paper addresses a real and important gap. The geospatial AI community has indeed developed raster and vector processing pipelines largely in isolation, and the argument for integration is compelling. If this perspective catalyzes research into truly multimodal geospatial foundation models, the impact could be substantial across:
However, perspective papers inherently have indirect impact—they influence through framing and agenda-setting rather than through tools, datasets, or methods that others can immediately build upon. The paper does not contribute a benchmark, dataset, codebase, or prototype system.
Timeliness & Relevance
The timing is excellent. The geospatial foundation model space is rapidly expanding (AlphaEarth, TerraMind, CopernicusFM, etc.), and the community is actively searching for the next frontier beyond raster-only pretraining. The availability of large-scale vector datasets (OpenStreetMap, Overture Maps) makes this direction practically feasible. Recent works like Poly2Vec, Geo2Vec, and AETHER demonstrate growing interest in vector representation learning. The paper correctly identifies that the field is at an inflection point where unified approaches could gain traction.
The emphasis on "human-centric" geospatial AI is timely given increasing interest in socioeconomic applications of satellite imagery (poverty mapping, urbanization tracking) where vector data about human systems is essential context.
Strengths & Limitations
Key Strengths:
1. Well-identified gap: The raster-vector dichotomy in geospatial AI is real and consequential. The paper makes a clear case for why this matters.
2. Comprehensive framing: The paper successfully surveys both raster-side (EOFMs) and vector-side (polymorphic encoding) developments, providing useful context for researchers entering the field.
3. Fairness considerations: The discussion of spatial fairness—particularly the bias in vector data toward high-income countries and urban areas versus the more uniform global coverage of raster data—is an important and often overlooked point.
4. Practical research agenda: The four research directions (methods, fairness, evaluation, uncertainty) are actionable and well-motivated.
Notable Limitations:
1. Lack of empirical grounding: No prototype, proof-of-concept, or even toy example demonstrates feasibility of the proposed unified architecture.
2. Architectural vagueness: The core technical proposal ("parallel token streams within shared transformer frameworks") is insufficiently specified. How exactly should heterogeneous geometries be tokenized alongside image patches? How should attention be computed between a polygon token and a set of image patch tokens?
3. Missing scalability analysis: No discussion of computational costs, memory requirements, or data curation effort needed for petabyte-scale joint pretraining.
4. Limited novelty beyond synthesis: While the synthesis is valuable, most individual ideas (cross-modal contrastive learning, vector tokenization, spatial indexing) have been proposed elsewhere. The paper's contribution is primarily in aggregating and framing these ideas.
5. Incomplete treatment of competing approaches: LLM-based approaches that could bridge modalities through natural language descriptions of both raster and vector data receive only brief mention.
Overall Assessment
This is a well-motivated perspective paper that identifies a genuine and important gap in geospatial AI research. It provides a useful synthesis of the current landscape and articulates a compelling vision for unified raster-vector representation learning. However, as a purely conceptual contribution without empirical validation, novel methods, or concrete benchmarks, its direct scientific impact is limited. The paper's influence will depend on whether the research community adopts the proposed agenda and whether the authors or others produce follow-up work that demonstrates the feasibility and benefits of the unified approach. The writing is clear but could be more precise in its technical proposals.
Generated Jun 2, 2026
Comparison History (16)
Paper 1 likely has higher near-term scientific impact: it introduces a concrete, open-source benchmark with defined metrics (completion, reaction time, resource use) and baseline evaluations, enabling reproducible comparison and driving measurable progress in long-running agent design. Its applications are broad across web agents and autonomous systems, and it is timely given current agent deployment challenges. Paper 2 is a compelling and potentially high-impact vision for multimodal geospatial foundation models, but as a perspective paper it offers less methodological rigor and fewer immediately testable artifacts, making impact more uncertain and longer-term.
Paper 1 offers higher potential scientific impact due to its profound real-world applications in Earth Observation, climate monitoring, and urban planning. While Paper 2 provides a valuable multi-agent benchmark for LLMs in a simulated game environment, Paper 1 proposes a fundamental paradigm shift in geospatial AI by unifying raster and vector data into foundation models. This enables planetary-scale, semantically grounded analysis of human and environmental systems, offering significantly broader interdisciplinary impact across geosciences, environmental studies, and AI compared to a gaming benchmark.
Paper 2 proposes a paradigm shift in geospatial AI by unifying raster and vector data modalities for foundation models, addressing a fundamental gap in Earth Observation. As a perspective paper, it has broader potential impact across remote sensing, urban planning, environmental science, and GIS communities. Its vision for joint spatial representation learning could reshape how geospatial foundation models are built. Paper 1, while solid, offers incremental improvement (3.7% success rate gain) on a specific LLM agent clarification task with narrower scope and applicability.
Paper 1 presents a concrete, technically novel method (score-level fusion of SSM importance into attention) with clear implementation advantages (single stock SDPA call, no custom kernels) and quantitative gains on established benchmarks, indicating methodological rigor and near-term adoptability. Its impact could extend broadly across efficient/long-context language modeling and hybrid architectures, making it timely in the current post-Transformer efficiency push. Paper 2 is a compelling, potentially high-impact vision for multimodal geospatial foundation models, but as a perspective paper it lacks demonstrated algorithms/results, reducing immediate scientific and practical impact relative to Paper 1.
Paper 1 proposes a fundamental paradigm shift in Earth Observation AI by unifying raster and vector data. As a perspective paper on foundation models, it has the potential to guide broad research directions across climate science, urban planning, and geography. While Paper 2 is methodologically rigorous, its focus on Remaining Useful Life prediction is a more niche industrial application, whereas Paper 1 addresses planetary-scale challenges with much broader interdisciplinary impact.
Paper 2 introduces a concrete, publicly available benchmark in the highly active field of LLM agents and tool use. AI benchmarks typically drive immediate, measurable progress and attract high citation counts. Paper 1, while conceptually valuable, is a perspective paper calling for future research directions, making its near-term scientific impact less direct and quantifiable compared to an open-source evaluation framework.
Paper 1 proposes a concrete, empirically validated algorithmic improvement (EKSFT) for LLM fine-tuning, directly addressing a critical bottleneck in the highly active field of AI alignment. Its methodological rigor and immediate applicability yield higher potential impact than Paper 2, which is primarily a perspective paper outlining future directions for geospatial foundation models rather than presenting a novel, tested methodology.
Paper 2 proposes a paradigm shift in geospatial foundation models by integrating raster and vector data modalities, addressing a fundamental limitation across the entire Earth Observation AI field. Its breadth of impact spans remote sensing, urban planning, environmental monitoring, and social sciences. As a perspective/position paper calling for unified spatial representation learning, it has potential to reshape research directions for a large community. Paper 1, while solid, addresses a narrower problem (multi-hop audio-visual reasoning benchmarks and an agentic framework) with more incremental contributions within the LLM reasoning space.
Paper 1 addresses a fundamental gap in geospatial AI by proposing integration of raster and vector data modalities for foundation models, which has broad implications across Earth observation, urban planning, environmental monitoring, and many applied domains. It builds on rapidly growing foundation model research and identifies a concrete, actionable research direction with clear practical applications. Paper 2, while interesting in proposing persona-based AI evaluation, addresses a narrower methodological concern in AI alignment evaluation. Its reliance on synthetic personas and the identified instability issues may limit near-term practical adoption. Paper 1's cross-disciplinary relevance and timeliness give it higher potential impact.
Paper 1 proposes a concrete, rigorously tested technical framework for safety steering in state-of-the-art diffusion models, addressing an urgent problem in generative AI with immediate real-world deployment value. In contrast, Paper 2 is a perspective piece outlining future directions for geospatial models; while visionary, it lacks the immediate methodological contribution and empirical validation that drive rapid scientific impact in machine learning.
Paper 2 presents a concrete, novel methodology (LLM + MaxSAT hybrid reasoning) with empirical validation showing substantial improvements (80%+ acceptance rates) over baselines. It addresses a well-recognized limitation of LLMs in constrained optimization with a verifiable approach applicable across robotics and planning domains. Paper 1, while addressing an important gap in geospatial AI, is a perspective/position paper proposing future directions rather than presenting implemented solutions with empirical results, limiting its immediate scientific impact despite the relevance of its vision.
Paper 2 has higher potential impact because it proposes a broadly applicable paradigm shift for geospatial foundation models: unifying raster EO imagery with vector semantics (e.g., OSM) in a joint embedding space. This targets a major, timely limitation in current EOFMs and could influence many downstream domains (urban planning, mobility, disaster response, climate, socioeconomic mapping), as well as multimodal representation learning more generally. Paper 1 is empirically rigorous and useful for HCI/LLM evaluation, but its impact is narrower and more descriptive, with less methodological or conceptual generalization beyond LLM usage analytics.
Paper 1 proposes a paradigm shift in geospatial AI by integrating raster and vector data modalities into unified spatial representation learning, addressing a fundamental limitation of current Earth Observation Foundation Models. Its breadth of impact spans remote sensing, urban computing, social sciences, and environmental monitoring. While Paper 2 addresses an important but narrower issue—evaluating benchmark quality for causal discovery—Paper 1's vision for next-generation geospatial foundation models has broader transformative potential, higher timeliness given the rapid growth of foundation models, and wider real-world applicability.
Paper 2 proposes a paradigm shift in geospatial AI by unifying raster and vector data modalities for foundation models, addressing a fundamental limitation across Earth Observation. Its breadth of impact spans environmental monitoring, urban planning, demographics, and geospatial AI broadly. As a perspective paper calling for a new research direction, it can catalyze an entire subfield. Paper 1, while technically strong with its ReSkill framework for agentic RL, addresses a more incremental improvement within a narrower scope of skill-augmented RL for LLM agents, limiting its cross-disciplinary impact.
Paper 2 has higher impact potential: it introduces a concrete, scalable training substrate (online generator+exact verifier) with 520 environments and demonstrates cross-model gains on multiple external benchmarks, suggesting methodological rigor and immediate applicability to multimodal/RL training. Its rule-verifiable, on-demand data generation addresses a timely bottleneck (static dataset limits) and can generalize across vision-language reasoning research. Paper 1 is a perspective proposing a unifying framework for raster+vector geospatial foundation models, potentially important but less immediately verifiable without new methods/results.
Paper 2 is more likely to have higher scientific impact because it proposes a concrete, timely framework (LLM agents + tools + safety constraints) for an under-studied but ubiquitous real-world gap in forecasting: incorporating weakly structured context to revise predictions. Its approach is broadly applicable across industries and domains that rely on forecasts, and aligns with active research on agentic LLM systems, auditability, and controllable decision support. Paper 1 is largely a perspective/call-to-action; while important and potentially high-impact long-term, it is less methodologically rigorous and less immediately actionable.