Конструювання запитів для класифікації земного покриву без навчальних прикладів за допомогою мультимодальних мовних моделей на знимках Sentinel-2

UKR: Класифікація земного покриву за супутниковими знімками є важливим завданням екологічного моніторингу, містобудівного планування та агрономії. Мультимодальні мовні моделі (VLM) дозволяють виконувати цю задачу без розмічених тренувальних даних, проте під час їх застосування виявлено системну проблему - хибну класифікацію за кольором сегментаційної маски (color leakage), коли модель ухвалює рішення не за вмістом зображення, а за довільним кольором маски. Метою роботи є розробка протоколу конструювання запитів для усунення цього явища та порівняння двох стратегій обробки супутникових знімків (багатокластерної та однокластерної). Запропоновано протокол із чотирьох інваріантів (TCI першим, сіра маска, заборона кольорових описів, фіксований JSON-формат) та зіставлено Варіант A (багатокластерний) і Варіант Б (однокластерний) на зображеннях Sentinel-2, що дозволило усунути хибну класифікацію за кольором маски та призвело до підвищення частки відповідей у коректному JSON-форматі (FCR) з ≈ 60 % до 97 %. Варіант Б досягає mIoU ≈ 13,2 %, що на 6,1 відсоткового пункту перевищує Варіант A; найкраща комбінація (UNet-encoder + GPT-4.1, Варіант Б) досягає 46,2 % mIoU.

ENG: Multimodal language models (VLMs) enable land cover classification from satellite imagery without labeled training data. This paper, extending previous work [8], analyzes prompt engineering approaches for land cover classification on Sentinel-2 imagery within the ESA WorldCover 2021 taxonomy. The color leakage phenomenon is identified and described, where the model bases its predictions on segmentation mask colors rather than image content. A four-invariant prompt protocol is proposed, including TCI-first ordering, grayscale mask conversion, elimination of color descriptions, and a fixed JSON output format, which removes this effect and increases the format compliance rate (FCR) from ≈60% to 97%. Two inference strategies are compared: Variant A (multi-cluster, mIoU ≈ 7.1%) and Variant B (single-cluster, mIoU ≈ 13.2%) on 10 Sentinel-2 tiles. In Variant B, each segment is processed independently using a binary mask, which simplifies spatial interpretation and reduces inter-segment interference. The highest result (mIoU = 46.2%) is achieved with the UNet-encoder + GPT-4.1 + Variant B configuration, although this corresponds to a single case. Problem Statement. Land cover mapping from satellite imagery is widely used in ecological monitoring, urban planning, and agronomy. Traditional semantic segmentation approaches require large labeled datasets and significant computational resources, especially when adapting to new regions. Recent multimodal language models, including GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.5 Pro, enable zero-shot classification without task-specific training. However, such pipelines introduce specific failure modes, notably the color leakage effect, where predictions depend on segmentation mask colors instead of actual image content. Recent Studies and Publications Analysis. VLMs are increasingly used in remote sensing owing to their capacity for open-vocabulary reasoning over satellite imagery. Yao et al. introduced Falcon, a remote sensing vision-language foundation model; Mall et al. developed RSVLM for satellite image understanding; Li et al. presented RS-CLIP for zero-shot scene classification. Liu et al. proposed RSHBench — a detailed benchmark for diagnosing hallucinations in multimodal LLMs applied to remote sensing. For zero-shot learning, Saha et al. demonstrated improved classification by adapting VLMs with attribute descriptions; Barzilai et al. analysed recipes for improving VLM zero-shot accuracy in remote sensing. In prompt engineering, Wei et al. established chain-of-thought prompting and White et al. catalogued reusable prompt patterns. Geirhos et al. documented shortcut learning in deep networks, providing theoretical grounding for the color leakage phenomenon. Despite these advances, systematic analysis of prompt design for eliminating color artifacts in VLM-based land cover classification remains unstudied. Research Objective. The objective of this study is to improve classification accuracy (mIoU) and structured output correctness (FCR) in zero-shot land cover classification on Sentinel-2 imagery by developing a prompt engineering protocol for multimodal language models that eliminates the color leakage effect and enforces a fixed structure of inputs and outputs. Main Body of Research. A two-stage processing pipeline is used, combining unsupervised segmentation with VLM-based classification under a four-invariant protocol: TCI-first ordering, grayscale mask, no color descriptions, and structured JSON output. Variant A performs classification of all segments in a single request, while Variant B processes each segment independently using a binary mask. This change in formulation improves mIoU from 7.1% to 13.2%. Ablation analysis (n = 5 tiles) shows that the JSON output constraint has the largest impact on FCR, while grayscale mask conversion most effectively reduces color leakage. Per-class analysis indicates that the improvement is primarily driven by the Cropland class (23.4% → 46.9%), whereas spectrally similar vegetation classes degrade. Conclusions. The study addresses the problem of improving classification accuracy (mIoU) and structured output correctness (FCR) in zero-shot land cover classification on Sentinel-2 satellite imagery through the development of a prompt engineering protocol for multimodal language models. The proposed protocol, consisting of four mandatory rules, eliminates the color leakage effect and increases FCR from ≈60% to 97%. It is shown that the use of the single-cluster processing strategy (Variant B), in which each segment is processed independently using a binary mask, improves classification accuracy from 7.1% to 13.2% compared to the multi-cluster strategy (Variant A). This approach eliminates inter-segment context contamination, simplifies segment interpretation for the model, and improves structured output correctness, as each request produces a single JSON object. The highest result (mIoU = 46.2%) is achieved with the UNet-encoder + GPT-4.1 + Variant B configuration; however, this corresponds to a single configuration and is not representative of overall performance across models and segmentation methods.