Update README.md

README.md

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   - split: train
     path: data/train-*
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+
+# MaSTr1325  
+**A Maritime Semantic Segmentation Training Dataset for Small‐Sized Coastal USVs**
+
+---
+
+## Overview  
+MaSTr1325 (Maritime Semantic Segmentation Training Dataset) is a large-scale collection of real-world images captured by an unmanned surface vehicle (USV) over a two-year span in the Gulf of Koper (Slovenia). It was specifically designed to advance obstacle-detection and segmentation methods in small-sized coastal USVs. All frames are per-pixel annotated into three main semantic categories—obstacles/environment, water, and sky—and synchronized with IMU measurements from the onboard sensors.  
+
+- **Total samples:** 1 325  
+- **Image resolution:** 1 278 × 958 px (captured at 10 FPS via stereo USB-2.0 cameras mounted 0.7 m above water)  
+- **Annotation categories (mask values):**  
+  - Obstacles & Environment = 0  
+  - Water = 1  
+  - Sky = 2  
+  - Ignore/Unknown = 4  
+
+Because marine scenes often feature clear water/sky regions, a high-grade per-pixel annotation process (20 min/image) was used to ensure boundary accuracy. Any ambiguous “edge” pixels between semantic regions were marked as “ignore” (value 4), so they can easily be excluded from training/validation.
+
+---
+
+## Authors & Citation  
+If you use MaSTr1325 in your work, please cite:
+
+```bibtex
+@inproceedings{bb_iros_2019,
+  title     = {The MaSTr1325 dataset for training deep USV obstacle detection models},
+  author    = {Bovcon, Borja and Muhovi{\v{c}}, Jon and Per{\v{s}}, Janez and Kristan, Matej},
+  booktitle = {{2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}},
+  year      = {2019},
+  pages     = {3431--3438},
+  organization = {IEEE}
+}
+
+@article{bb_ras_2018,
+  title     = {Stereo obstacle detection for unmanned surface vehicles by IMU‐assisted semantic segmentation},
+  author    = {Bovcon, Borja and Muhovi{\v{c}}, Jon and Per{\v{s}}, Janez and Kristan, Matej},
+  journal   = {Robotics and Autonomous Systems},
+  volume    = {104},
+  pages     = {1--13},
+  year      = {2018},
+  publisher = {Elsevier}
+}