AEF-Econ: Toward Plug-and-Play Socioeconomic Foundation Embeddings from AlphaEarth for Urban Remote Sensing

• URL: http://arxiv.org/abs/2606.20697v1
• Date: Mon, 15 Jun 2026 08:43:25 GMT
• Title: AEF-Econ: Toward Plug-and-Play Socioeconomic Foundation Embeddings from AlphaEarth for Urban Remote Sensing
• Abstract summary: We integrate seven heterogeneous data streams across 36 Chinese cities over eight years.<n>We construct CHN-Econ, a socioeconomic benchmark with 16 labels in three categories.<n>We infer 14.4 million pixels across 36 cities and eight years and release AEF-Econ, including 128d and 64d compressed versions.
• Score: 11.377452084318987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AlphaEarth Foundations (AEF) unify global remote sensing foundation embeddings through multimodal self-supervised learning, but their pretraining focuses on physical land-surface signals, limiting plug-and-play use in socioeconomic tasks. We integrate seven heterogeneous data streams across 36 Chinese cities over eight years - AEF embeddings, population, nighttime lights, remote sensing indices, points of interest (POIs), urban morphology, and cross-lingual text - and construct CHN-Econ, a socioeconomic benchmark with 16 labels in three categories. We conduct 31 controlled experiments along five axes: fusion architecture, self-supervised objective, text integration, embedding dimensionality, and normalization. Used alone as a linear probe, AEF achieves R2 values of only 0.301 for cross-region and 0.160 for cross-tier evaluation. The five-axis ablated backbone improves these scores to 0.832 and 0.671, respectively, but reveals that low-dimensional semantic streams are consistently suppressed by high-dimensional streams under shared reconstruction. To address this bottleneck, we propose Capacity-Adaptive Reconstruction (CAR), replacing shared reconstruction with per-stream decoders and stream-level losses to mitigate inter-stream capacity competition. CAR further raises cross-region and cross-tier R2 to 0.848 and 0.693, and restores collapsed labels from negative R2 to a stable range. Using CAR, we infer 14.4 million pixels across 36 cities and eight years and release AEF-Econ, including 128d and 64d compressed versions. Self-diagnostics and case studies show that AEF-Econ captures cross-city hierarchies and intra-urban spatial organization under unsupervised settings, providing a socioeconomic remote sensing foundation embedding complementary to AEF physical embeddings.

Related papers

• BEACON: Behavioral and Semantic Enrichment of AlphaEarth Embeddings through Tri-Modal Contrastive Learning [9.468116544984651]
  We propose BEACON, a tri-modal contrastive learning framework that aligns three views of urban space.<n> BEACON improves relative R2 over AlphaEarth by up to 43% for obesity prevalence, 34% for poor mental health, and 22% for median household income.<n>These findings highlight the value of augmenting geospatial foundation models with semantic and behavioral signals.
  arXiv  Detail & Related papers  (2026-08-30T04:46:23Z)
• Rethinking Attention Locality in Spiking Transformers [50.00501275379363]
  Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computation.<n>Their Softmax-free Spiking Self-Attention (SSA) struggles to establish spatially localized token interactions.<n>We propose Spatially Contiguous Local Attention with Boundary Continuity Pathway (SCLA-BCP)<n>Our approach improves mAP@50 by up to 9.50% on COCO on 2017 and mIoU by up to 3.42% on ADE20K.
  arXiv  Detail & Related papers  (2026-08-09T07:36:17Z)
• LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation [53.88286427894783]
  Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context.<n>Yet, their computational footprint typically confines them to cloud execution, hindering low-latency inference with local deployment on resource-constrained robots.<n>We present a distillation strategy that transfers complex spatial-semantic reasoning from large frontier models into a lightweight, 4B- parameter local VLM for edge execution on embedded GPU devices.
  arXiv  Detail & Related papers  (2026-06-26T09:11:59Z)
• URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification [2.538209532048867]
  KinemaForge is a constraint-driven pipeline that infers part-level shape, joint topology, and joint parameters from short RGB-D sequences.<n>It reduces the average joint-axis error from 4.52 degrees to 2.83 degrees (-37.4%) over the strongest geometric baseline.<n>KinemaForge yields URDFs whose closed-loop manipulation success rate improves by 14.6 percentage points over Ditto.
  arXiv  Detail & Related papers  (2026-06-17T09:42:25Z)
• Slum Detection and Density Mapping with AlphaEarth Foundations: A Representation Learning Evaluation Across 12 Global Cities [9.982796078979648]
  Pixel-level slum mapping has long been constrained by limited cross-city generalisation.<n>AlphaEarth Foundations offers a new analysis-ready basis for lightweight slum monitoring.<n>We evaluate AEF on slum classification and sub-pixel density estimation across 12 cities and 69 city-year pairs.
  arXiv  Detail & Related papers  (2026-05-11T05:54:15Z)
• SpatialFly: Geometry-Guided Representation Alignment for UAV Vision-and-Language Navigation in Urban Environments [49.966170814478915]
  We propose a geometry-guided spatial representation framework for UAV VLN.<n>Running on RGB observations without explicit 3D reconstruction, SpatialFly introduces a geometry-guided 2D representation alignment mechanism.<n> Experimental results show that SpatialFly consistently outperforms state-of-the-art UAV VLN baselines across both seen and unseen environments.
  arXiv  Detail & Related papers  (2026-03-22T03:56:58Z)
• DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning [62.347535250646196]
  This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling.<n>DeFRiS integrates three innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer across heterogeneous silos; (ii) a silo-optimized local learning mechanism combining Generalized Advantage Estimation with clipped policy updates to resolve sparse delayed reward challenges; and (iii) a Dual-Track Non-IID robust decentralized aggregation protocol leveraging gradient fingerprints for similarity-aware knowledge transfer and anomaly detection.
  arXiv  Detail & Related papers  (2026-03-16T02:02:38Z)
• Physically Interpretable AlphaEarth Foundation Model Embeddings Enable LLM-Based Land Surface Intelligence [0.0]
  We present a comprehensive interpretability analysis of Google AlphaEarth's 64-dimensional embeddings against 26 environmental variables.<n>We then developed a Land Surface Intelligence system that implements retrieval-augmented generation over a FAISS-indexed embedding database of 12.1 million vectors.
  arXiv  Detail & Related papers  (2026-02-10T22:58:50Z)
• Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring [19.019853798955513]
  ESD is an ultra-lightweight, 30-m global Earth embedding database spanning the 25-year period from 2000 to 2024.<n>The dataset achieves a transformative 340-fold reduction in data volume compared to raw archives.<n>With robust few-shot learning capabilities and longitudinal consistency, ESD provides a versatile foundation for democratizing planetary-scale research.
  arXiv  Detail & Related papers  (2026-01-16T10:59:43Z)
• Geodiffussr: Generative Terrain Texturing with Elevation Fidelity [48.82552523546255]
  We introduce Geodiffussr, a flow-matching pipeline that synthesizes text-guided texture maps.<n>The core mechanism is multi-scale content aggregation (MCA): DEM features are injected into UNet blocks at multiple resolutions to enforce global-to-local elevation consistency.<n>To train and evaluate Geodiffussr, we assemble a globally distributed, biome- and climate-stratified corpus of triplets pairing SRTM-derived DEMs with Sentinel-2 imagery and vision-grounded natural-appearance captions.
  arXiv  Detail & Related papers  (2025-11-28T09:52:44Z)
• Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement [6.518460105727812]
  We propose Federated Local Data-Infused Graph Creation with Node-centric Model Refinement (Fed-LDR) algorithm. Fed-LDR leverages FL and Graph Convolutional Networks (GCN) to enhance spatial-temporal data analysis in urban environments. We show that Fed-LDR achieved the lowest Mean Absolute Error (MAE) values of 20.15 and 17.30, and the lowest Root Mean Square Error (RMSE) values of 32.30 and 27.15, respectively.
  arXiv  Detail & Related papers  (2024-11-07T18:13:31Z)
• Towards Label-free Scene Understanding by Vision Foundation Models [87.13117617056004]
  We investigate the potential of vision foundation models in enabling networks to comprehend 2D and 3D worlds without labelled data. We propose a novel Cross-modality Noisy Supervision (CNS) method that leverages the strengths of CLIP and SAM to supervise 2D and 3D networks simultaneously. Our 2D and 3D network achieves label-free semantic segmentation with 28.4% and 33.5% mIoU on ScanNet, improving 4.7% and 7.9%, respectively.
  arXiv  Detail & Related papers  (2023-06-06T17:57:49Z)
• Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles [63.20765930558542]
  3D semantic data are useful for core perception tasks such as obstacle detection and ego-vehicle localization. We propose a new dataset, Navya 3D (Navya3DSeg), with a diverse label space corresponding to a large scale production grade operational domain. It contains 23 labeled sequences and 25 supplementary sequences without labels, designed to explore self-supervised and semi-supervised semantic segmentation benchmarks on point clouds.
  arXiv  Detail & Related papers  (2023-02-16T13:41:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.