Unsupervised Urban Tree Biodiversity Mapping from Street-Level Imagery Using Spatially-Aware Visual Clustering

• URL: http://arxiv.org/abs/2508.13814v3
• Date: Sun, 24 Aug 2025 14:39:11 GMT
• Title: Unsupervised Urban Tree Biodiversity Mapping from Street-Level Imagery Using Spatially-Aware Visual Clustering
• Abstract summary: Urban tree biodiversity is critical for climate resilience, ecological stability, and livability in cities.<n>Field-based inventories provide reliable estimates of Shannon and Simpson diversity but are costly and time-consuming.<n>We introduce an unsupervised clustering framework that integrates visual embeddings from street-level imagery with spatial planting patterns to estimate biodiversity without labels.
• Score: 2.757627877483678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban tree biodiversity is critical for climate resilience, ecological stability, and livability in cities, yet most municipalities lack detailed knowledge of their canopies. Field-based inventories provide reliable estimates of Shannon and Simpson diversity but are costly and time-consuming, while supervised AI methods require labeled data that often fail to generalize across regions. We introduce an unsupervised clustering framework that integrates visual embeddings from street-level imagery with spatial planting patterns to estimate biodiversity without labels. Applied to eight North American cities, the method recovers genus-level diversity patterns with high fidelity, achieving low Wasserstein distances to ground truth for Shannon and Simpson indices and preserving spatial autocorrelation. This scalable, fine-grained approach enables biodiversity mapping in cities lacking detailed inventories and offers a pathway for continuous, low-cost monitoring to support equitable access to greenery and adaptive management of urban ecosystems.

Related papers

• Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features [0.0]
  We show that self-supervised learning applied to satellite imagery can predict below-ground ectomycorrhizal fungal richness.<n>Our models explain over half the variance in species richness across 12,000 field samples spanning Europe and Asia.<n>These results establish SSL satellite features as a scalable tool for extending sparse field observations to continuous, high-resolution biodiversity maps.
  arXiv  Detail & Related papers  (2026-04-10T18:50:14Z)
• Multimodal Urban Tree Detection from Satellite and Street-Level Imagery via Annotation-Efficient Deep Learning Strategies [0.7734726150561086]
  This study introduces a framework that integrates high-resolution satellite imagery with ground-level Google Street View to enable scalable and detailed urban tree detection.<n>The framework first leverages satellite imagery to localize tree candidates and then retrieves targeted ground-level views for detailed detection.<n>To address the annotation bottleneck, domain adaptation is used to transfer knowledge from an existing annotated dataset to a new region of interest.
  arXiv  Detail & Related papers  (2026-04-03T23:05:59Z)
• Earth Embeddings Reveal Diverse Urban Signals from Space [17.134313498337804]
  We benchmark three Earth embedding families, AlphaEarth, Prithvi, and Clay, for urban signal prediction.<n>Using a unified supervised-learning framework, we predict 14 neighborhood-level indicators spanning crime, income, health, and travel behavior.<n>Results show that Earth embeddings capture substantial urban variation, with the highest predictive skill for outcomes tied to built-environment structure.
  arXiv  Detail & Related papers  (2026-04-03T20:58:37Z)
• StreetTree: A Large-Scale Global Benchmark for Fine-Grained Tree Species Classification [10.733169445873289]
  StreetTree is the world's first large-scale benchmark dataset dedicated to fine-grained street tree classification.<n>The dataset contains over 12 million images covering more than 8,300 common street tree species.<n>StreetTree poses substantial challenges for pretrained vision models under complex urban environments.
  arXiv  Detail & Related papers  (2026-02-22T10:43:43Z)
• Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting [99.4484686548807]
  We propose OmniAir, a semantic topology learning framework tailored for global station-level prediction.<n>Our approach effectively captures long-range non-Euclidean correlations and physical diffusion patterns across unevenly distributed global networks.<n>Experiments show that OmniAir achieves state-of-the-art performance against 18 baselines, maintaining high efficiency and scalability with speeds nearly 10 times faster than existing models.
  arXiv  Detail & Related papers  (2026-01-29T15:58:07Z)
• Continental scale habitat modelling with artificial intelligence and multimodal earth observation [0.0]
  Habitats integrate the abiotic conditions and biophysical structures that support biodiversity and sustain nature's contributions to people.<n>Current maps often fall short in thematic or spatial resolution because they must model several mutually exclusive habitat types.<n>Here, we evaluated how high-resolution remote sensing (RS) data and Artificial Intelligence (AI) tools can improve habitat classification over large geographic extents.
  arXiv  Detail & Related papers  (2025-07-13T18:11:26Z)
• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• Using street view imagery and deep generative modeling for estimating the health of urban forests [0.0]
  Healthy urban forests play a crucial role in mitigating climate change.<n>Traditional approaches for monitoring the health of urban forests require instrumented inspection techniques.<n>We propose an alternative approach for monitoring the urban forests using simplified inputs.
  arXiv  Detail & Related papers  (2025-04-20T12:09:15Z)
• Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments [57.59857784298534]
  We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images.<n>This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes.
  arXiv  Detail & Related papers  (2025-03-06T05:13:19Z)
• Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
  We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.<n>Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.<n>Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
  arXiv  Detail & Related papers  (2024-10-14T17:22:55Z)
• Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation [2.3429628556845405]
  Fine-tuning of a geospatial foundation model to estimate above-ground biomass has comparable performance to a U-Net trained from scratch. We also explore the transfer-learning capabilities of the models by fine-tuning on satellite imagery with sparse labels from different eco-regions in Brazil.
  arXiv  Detail & Related papers  (2024-06-28T12:54:10Z)
• FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems [56.0640340392818]
  We introduce a framework, FREE, that enables the use of varying features and available information to train a universal model.<n>The core idea is to map available environmental data into a text space and then convert the traditional predictive modeling task in environmental science to a semantic recognition problem.<n>Our evaluation on two societally important real-world applications, stream water temperature prediction and crop yield prediction, demonstrates the superiority of FREE over multiple baselines.
  arXiv  Detail & Related papers  (2023-11-17T00:53:09Z)
• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• Bird Distribution Modelling using Remote Sensing and Citizen Science data [31.375576105932442]
  Climate change is a major driver of biodiversity loss. There are significant knowledge gaps about the distribution of species. We propose an approach leveraging computer vision to improve species distribution modelling.
  arXiv  Detail & Related papers  (2023-05-01T20:27:11Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)

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.