arXiv:2512.02290v1 Announce Type: new 
Abstract: Deep learning models for SAR oil spill segmentation often fail to generalize across regions due to differences in sea-state, backscatter statistics, and slick morphology, a limitation that is particularly severe along the Peruvian coast where labeled Sentinel-1 data remain scarce. To address this problem, we propose \textbf{MORP--Synth}, a two-stage synthetic augmentation framework designed to improve transfer from Mediterranean to Peruvian conditions. Stage~A applies Morphological Region Perturbation, a curvature guided label space method that generates realistic geometric variations of oil and look-alike regions. Stage~B renders SAR-like textures from the edited masks using a conditional generative INADE model. We compile a Peruvian dataset of 2112 labeled 512$\times$512 patches from 40 Sentinel-1 scenes (2014--2024), harmonized with the Mediterranean CleanSeaNet benchmark, and evaluate seven segmentation architectures. Models pretrained on Mediterranean data degrade from 67.8\% to 51.8\% mIoU on the Peruvian domain; MORP--Synth improves performance up to +6 mIoU and boosts minority-class IoU (+10.8 oil, +14.6 look-alike).

تم تقديم إطار عمل جديد يسمى MORP--Synth لتعزيز تقسيم تسرب النفط في صور الرادار ذات الفتحة الاصطناعية (SAR)، حيث يتناول التحديات التي تواجه الساحل البيروفي بسبب نقص البيانات المصنفة. تستخدم هذه العملية ذات المرحلتين اضطراب المنطقة الشكلية لإنشاء تباينات واقعية لزيوت النفط وتستخدم نموذجًا توليديًا شرطيًا لتوليد قوام مشابه لـ SAR من هذه التباينات.

Se ha introducido un nuevo marco llamado MORP--Synth para mejorar la segmentación de derrames de petróleo en imágenes de radar de apertura sintética (SAR), abordando especialmente los desafíos que enfrenta la costa peruana debido a la escasez de datos etiquetados. Este proceso en dos etapas utiliza la Perturbación de Región Morfológica para crear variaciones realistas de las manchas de petróleo y emplea un modelo generativo condicional para generar texturas similares a SAR a partir de estas variaciones.

Un nouveau cadre nommé MORP--Synth a été introduit pour améliorer la segmentation des déversements de pétrole dans les images radar à synthèse d'ouverture (SAR), en particulier face aux défis rencontrés le long de la côte péruvienne en raison de données étiquetées limitées. Ce processus en deux étapes utilise la perturbation de région morphologique pour créer des variations réalistes des nappes de pétrole et emploie un modèle génératif conditionnel pour générer des textures similaires à celles du SAR à partir de ces variations.

A new framework named MORP--Synth has been introduced to enhance oil spill segmentation in synthetic aperture radar (SAR) imagery, particularly addressing the challenges faced along the Peruvian coast due to limited labeled data. This two-stage process employs Morphological Region Perturbation to create realistic variations of oil slicks and uses a conditional generative model to generate SAR-like textures from these variations.

Enhancing Cross Domain SAR Oil Spill Segmentation via Morphological Region Perturbation and Synthetic Label-to-SAR Generation

arXiv:2512.02055v1 Announce Type: new 
Abstract: Floods are among the most damaging weather-related hazards, and in 2024, the warmest year on record, extreme flood events affected communities across five continents. Earth observation (EO) satellites provide critical, frequent coverage for mapping inundation, yet operational accuracy depends heavily on labeled datasets and model generalization. Recent Geospatial Foundation Models (GFMs), such as ESA-IBM's TerraMind, offer improved generalizability through large-scale self-supervised pretraining, but their performance on diverse global flood events remains poorly understood.
  We fine-tune TerraMind for flood extent mapping using FloodsNet, a harmonized multimodal dataset containing co-located Sentinel-1 (Synthetic Aperture Radar, SAR data) and Sentinel-2 (optical) imagery for 85 flood events worldwide. We tested four configurations (base vs. large models; frozen vs. unfrozen backbones) and compared against the TerraMind Sen1Floods11 example and a U-Net trained on both FloodsNet and Sen1Floods11. The base-unfrozen configuration provided the best balance of accuracy, precision, and recall at substantially lower computational cost than the large model. The large unfrozen model achieved the highest recall. Models trained on FloodsNet outperformed the Sen1Floods11-trained example in recall with similar overall accuracy. U-Net achieved higher recall than all GFM configurations, though with slightly lower accuracy and precision.
  Our results demonstrate that integrating multimodal optical and SAR data and fine-tuning a GFM can enhance near-real-time flood mapping. This study provides one of the first global-scale evaluations of a GFM for flood segmentation, highlighting both its potential and current limitations for climate adaptation and disaster resilience.

تم استغلال التقدمات الأخيرة في نماذج الأساس الجغرافية متعددة الوسائط المعتمدة على الذكاء الاصطناعي، وخاصة نموذج TerraMind من ESA-IBM، لتحسين رسم خرائط الفيضانات في الوقت شبه الحقيقي على مستوى العالم. يأتي هذا التطور في ضوء الأحداث الشديدة للفيضانات التي أثرت على المجتمعات عبر خمس قارات في عام 2024، وهو العام الأكثر حرارة على الإطلاق. تقوم الدراسة بضبط TerraMind باستخدام FloodsNet، وهو مجموعة بيانات متعددة الوسائط تجمع بين صور Sentinel-1 وSentinel-2 لـ 85 حدث فيضان حول العالم.

Los avances recientes en modelos de fundación geoespacial multimodal basados en IA, particularmente TerraMind de ESA-IBM, se han aprovechado para mejorar la cartografía de inundaciones en tiempo casi real a nivel global. Este desarrollo surge a raíz de eventos de inundación extremos que afectaron a comunidades en cinco continentes en 2024, el año más cálido registrado. El estudio ajusta TerraMind utilizando FloodsNet, un conjunto de datos multimodal que combina imágenes de Sentinel-1 y Sentinel-2 para 85 eventos de inundación en todo el mundo.

Des avancées récentes dans les modèles de fondation géospatiale multimodale basés sur l'IA, en particulier TerraMind d'ESA-IBM, ont été exploitées pour améliorer la cartographie des inondations en temps quasi réel à l'échelle mondiale. Ce développement intervient à la suite d'événements d'inondation extrêmes touchant des communautés sur cinq continents en 2024, l'année la plus chaude jamais enregistrée. L'étude ajuste TerraMind en utilisant FloodsNet, un ensemble de données multimodal combinant des images Sentinel-1 et Sentinel-2 pour 85 événements d'inondation dans le monde.

Recent advancements in AI multimodal geospatial foundation models, particularly ESA-IBM's TerraMind, have been leveraged for enhanced near-real-time flood mapping globally. This development comes in light of extreme flood events impacting communities across five continents in 2024, the warmest year on record. The study fine-tunes TerraMind using FloodsNet, a multimodal dataset combining Sentinel-1 and Sentinel-2 imagery for 85 flood events worldwide.

Enhancing Cross Domain SAR Oil Spill Segmentation via Morphological Region Perturbation and Synthetic Label-to-SAR Generation

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