Local and Indigenous Knowledge Systems

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Workshop Papers

Venue Title
NeurIPS 2025 Bugs in Citizen-Science Data: Robust Biodiversity AI Begins with Clean Images (Papers Track)
Abstract and authors: (click to expand)

Abstract: Despite bold claims that AI will accelerate scientific discovery, domains like climate change research still face challenges in learning from real-world data. We propose a data preprocessing pipeline that addresses a key bottleneck in biodiversity monitoring: the lack of standardized image quality control in large-scale species datasets. As climate change drives shifts in ecosystems, accurate species identification is critical. Yet citizen science images, though rich in species diversity, are often noisy and inconsistent. We systematically filters such data using classical heuristics and Vision-Language Model (VLM)-based image quality assessment to detect poor composition, human presence, and multiple-species interference. Zero-shot benchmarks with state-of-the-art biodiversity fine-tuned foundation models on filtered datasets of visually similar plant species demonstrate that data quality significantly affects AI reliability. With this work, we highlight a core limitation in biodiversity AI and encourage broader exploration of quality-related bottlenecks in biodiversity monitoring. Code is available at the project website.

Authors: Nikita Gavrilov (Fontys University of Applied Science); Gerard Schouten (Fontys University of Applied Science); Georgiana Manolache (Fontys University of Applied Science)
NeurIPS 2025 Geospatial Chain of Thought Reasoning for Enhanced Visual Question Answering on Satellite Imagery (Papers Track)
Abstract and authors: (click to expand)

Abstract: Geospatial chain of thought (CoT) reasoning is essential for advancing Visual Question Answering (VQA) on satellite imagery, particularly in climate related applications such as disaster monitoring, infrastructure risk assessment, urban resilience planning, and policy support. Existing VQA models enable scalable interpretation of remote sensing data but often lack the structured reasoning required for complex geospatial queries. We propose a VQA framework that integrates CoT reasoning with Direct Preference Optimization (DPO) to improve interpretability, robustness, and accuracy. By generating intermediate rationales, the model better handles tasks involving detection, classification, spatial relations, and comparative analysis, which are critical for reliable decision support in high stakes climate domains. Experiments show that CoT supervision improves accuracy by 34.9% over direct baselines, while DPO yields additional gains in accuracy and reasoning quality. The resulting system advances VQA for multispectral Earth observation by enabling richer geospatial reasoning and more effective climate use cases.

Authors: Shambhavi Shanker (IIT Bombay); Manikandan Padmanaban (IBM Research India); Jagabondhu Hazra (IBM Research India)
NeurIPS 2022 ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Restoring ecosystems and reducing deforestation are necessary tools to mitigate the anthropogenic climate crisis. Current measurements of forest carbon stock can be inaccurate, in particular for underrepresented and small-scale forests in the Global South, hindering transparency and accountability in the Monitoring, Reporting, and Verification (MRV) of these ecosystems. There is thus need for high quality datasets to properly validate ML-based solutions. To this end, we present ForestBench, which aims to collect and curate geographically-balanced gold-standard datasets of small-scale forest plots in the Global South, by collecting ground-level measurements and visual drone imagery of individual trees. These equitable validation datasets for ML-based MRV of nature-based solutions shall enable assessing the progress of ML models for estimating above-ground biomass, ground cover, and tree species diversity.

Authors: Lucas Czech (Carnegie Institution for Science); Björn Lütjens (MIT); David Dao (ETH Zurich)
AAAI FSS 2022 Discovering Transition Pathways Towards Coviability with Machine Learning
Abstract and authors: (click to expand)

Abstract: This paper presents our ongoing French-Brazilian collaborative project which aims at: (1) establishing a diagnosis of socio-ecological coviability for several sites of interest in Nordeste, the North-East region of Brazil (in the states of Paraiba, Ceara, Pernambuco, and Rio Grande do Norte known for their biodiversity hotspots and vulnerabilities to climate change) using advanced data science techniques for multisource and multimodal data fusion and (2) finding transition pathways towards coviability equilibrium using machine learning techniques. Data collected in the field by scientists, ecologists, local actors combined with volunteered information, pictures from smart-phones, and data available on-line from satellite imagery, social media, surveys, etc. can be used to compute various coviability indicators of interest for the local actors. These indicators are useful to characterize and monitor the socio-ecological coviability status along various dimensions of anthropization, human welfare, ecological and biodiversity balance, and ecosystem intactness and vulnerabilities.

Authors: Laure Berti-Equille (IRD) and Rafael Raimundo (UFPB)