Update English model card

README.md

@@ -1,19 +1,16 @@
 ---
-frameworks: PyTorch
+license: other
 language:
 - en
-license: apache-2.0
 tags:
 - OneScience
 - Earth Science
-- Remote Sensing
 - Multi-Modal Remote Sensing
 - Semantic Segmentation
-- SkySense
-- arxiv:2312.10115
-tasks: []
+frameworks: PyTorch
 datasets: []
 ---
+
 <p align="center">
   <strong>
     <span style="font-size: 30px;">SkySense</span>
@@ -22,50 +19,48 @@ datasets: []
 
 # Model Introduction
 
-SkySense is a multi-modal and temporal remote sensing foundation model for universal Earth observation interpretation. It jointly models high-resolution optical imagery, Sentinel-2 multispectral time series, Sentinel-1 SAR time series, and geographic context to provide unified representations for remote sensing classification, segmentation, detection, and change detection.
-
-Paper: SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
+SkySense is a multimodal remote sensing foundation model for general Earth observation interpretation. It jointly represents high-resolution RGB, Sentinel-1, and Sentinel-2 data through independent spatial encoding, temporal aggregation, cross-modal fusion, and geographic prototype modeling.
 
+Paper: SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery  
 https://arxiv.org/abs/2312.10115
 
 # Model Description
 
-SkySense was proposed by Ant Group, Wuhan University, and MYbank. The original model was trained on 21.5 million groups of high-resolution optical, Sentinel-2 multispectral, and Sentinel-1 SAR temporal samples, and is suitable for single-modal or multi-modal, static or temporal remote sensing classification and localization tasks.
+SkySense was proposed by a research team from Wuhan University, Huazhong University of Science and Technology, and other institutions. The model is trained with large-scale multimodal remote sensing time-series data and fuses high-resolution RGB, Sentinel-1 radar, and Sentinel-2 multispectral observations. It is suitable for multimodal remote sensing representation learning, land-cover semantic segmentation, and other Earth observation interpretation tasks.
 
 # Use Cases
 
 | Scenario | Description |
 | :---: | :--- |
-| Multi-modal remote sensing representation training | Train SkySense with multi-modal temporal remote sensing image data. |
-| Remote sensing image semantic segmentation | Fuse optical, multispectral, and SAR features to predict land-cover classes for image pixels. |
-| Local quick validation | Use synthetic data to validate data loading, training, inference, evaluation, and visualization. |
-| Hugging Face / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. |
-| Multi-GPU training | Launch distributed multi-process training with `torchrun`. |
+| Multimodal remote sensing fusion | Fuse multi-resolution observations from HR, Sentinel-1, and Sentinel-2. |
+| Multi-temporal modeling | Process satellite sequences using date encoding and intra-modality temporal aggregation. |
+| Semantic segmentation | Produce high-resolution land-cover class maps. |
+| Geographic object detection | Transfer and fine-tune multimodal representations for detecting remote sensing objects such as aircraft, ships, and vehicles. |
+| Land-surface change detection | Adapt multi-temporal observations to downstream tasks to identify changes in buildings and land-cover regions. |
+| Local engineering validation | Use a small amount of synthetic data to check the training, inference, and evaluation workflows. |
 
 # Usage Guide
 
-## 1. OneCode Usage
+## 1. OneCode
 
 Experience intelligent one-click AI4S programming through the OneCode online environment:
 
 [Click to Experience Intelligent One-Click AI4S Programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)
 
-## 2. Manual Installation and Usage
-
-**Hardware Requirements**
-
-- A GPU or DCU is recommended.
-- CPU can be used for import and small-scale connectivity verification; full training and inference will be slower.
-- DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching the cluster, is recommended.
-
-### Download the Model Package
+## 2. Download and Installation
 
 ```bash
 hf download OneScience-Group/SkySense --local-dir ./SkySense
 cd SkySense
 ```
 
-### Install the Runtime Environment
+### Environment Dependencies
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- CPU can be used for small-configuration connectivity validation; full training and inference will be slow.
+- DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching the current cluster, is recommended.
 
 **DCU Environment**
 
@@ -89,42 +84,45 @@ pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simp
 
 ### Training Data Introduction
 
-The original SkySense pretraining collection contains about 21.5 million spatially registered multi-modal remote sensing samples. It includes high-resolution optical imagery, Sentinel-2 multispectral temporal imagery, Sentinel-1 SAR temporal imagery, acquisition dates, geographic region information, and downstream task labels. The complete registered pretraining collection is not publicly available from the authors, and it is not included in this model repository.
+By default, 2 training and 1 test synthetic samples are used to validate the engineering workflow. They are saved as `data/train.npz` and `data/test.npz`, respectively.
 
-Original data sources include Sentinel-1 and Sentinel-2 products available through Copernicus Data Space and commercial high-resolution imagery such as WorldView. Users must follow the applicable data licenses and independently complete cloud filtering, radiometric processing, spatial registration, resampling, cropping, normalization, and label preparation.
+The synthetic data preserves the specifications from the paper and the official backbone: static HR, 20 Sentinel-2 time steps, 10 Sentinel-1 time steps, and the channel counts and spatial sizes of each modality.
 
-The default local validation uses synthetic NPZ data:
+Real data must be preprocessed and converted to the following NPZ training protocol. This protocol is consistent with the model input specification but is not the download format of the original datasets.
 
-```bash
-python scripts/fake_data.py
+```text
+hr: float32 [N,1,3,224,224]
+s2: float32 [N,20,10,64,64]
+s1: float32 [N,10,2,64,64]
+dates_hr: int64 [N,1]
+dates_s2: int64 [N,20]
+dates_s1: int64 [N,10]
+region: int64 [N]
+labels: int64 [N,224,224]
+band_order_hr: string [3]
+band_order_s2: string [10]
+band_order_s1: string [2]
 ```
 
-The command creates 24 training samples and 8 test samples. Synthetic data is used only to validate the complete training, inference, evaluation, and visualization pipeline. Synthetic results are not reported as model performance and do not reproduce the paper experiments.
-
-For real-data training, prepare the corresponding multi-modal samples in the format expected by the scripts, save them under `data/`, update the data and model settings in `conf/config.yaml`, and use the same `scripts/train.py`. Do not run `scripts/fake_data.py` when using real data.
-
-### Training
-
-Synthetic-data quick validation:
+`fake_data.py` automatically writes the `protocol` and `data_source` protocol metadata. These fields must be retained when using real data.
 
 ```bash
 python scripts/fake_data.py
-python scripts/train.py
 ```
 
-Single GPU or CPU:
+### Training
 
 ```bash
 python scripts/train.py
 ```
 
-Multi-GPU:
+For multi-GPU training, use:
 
 ```bash
-torchrun --nproc_per_node=8 --nnodes=1 --rdzv_id=1000 --rdzv_backend=c10d --max_restarts=0 --master_addr="localhost" --master_port=29500 scripts/train.py
+torchrun --nproc_per_node=8 scripts/train.py
 ```
 
-Training outputs:
+Training jointly optimizes semantic segmentation and cross-modal representation alignment objectives and saves a checkpoint and aggregate training metrics. The default configuration is intended for quick workflow validation. Formal experiments should use the multimodal time-series data, model configuration, and training duration corresponding to the paper.
 
 ```text
 result/checkpoints/skysense.pt
@@ -133,7 +131,7 @@ result/training/metrics.json
 
 ### Training Weights
 
-Pretrained weights based on multi-modal temporal remote sensing data will be provided under `weight/`. The weight file will be uploaded in a future update. Checkpoints generated from synthetic data are only for pipeline validation and do not provide real remote sensing interpretation capability.
+This repository will provide SkySense training weights in the `weight/` folder. The weight files will be uploaded soon and are expected to be available in the near future.
 
 ### Inference
 
@@ -141,9 +139,7 @@ Pretrained weights based on multi-modal temporal remote sensing data will be pro
 python scripts/inference.py
 ```
 
-Inference reads `data/test.npz` and `result/checkpoints/skysense.pt`. Test data must use the same channel order, normalization, spatial size, region mapping, and class definition as the training data.
-
-Prediction output:
+Inference loads the training checkpoint, generates high-resolution semantic segmentation results in batches, and saves them to:
 
 ```text
 result/output/
@@ -155,15 +151,13 @@ result/output/
 python scripts/result.py
 ```
 
-Evaluation and visualization outputs:
+Evaluation reports pixel accuracy, per-class IoU, and mean IoU, and generates a comparison figure of input, label, and prediction. Results on synthetic data are only for engineering workflow validation and do not represent full-paper performance.
 
 ```text
 result/evaluation/metrics.json
 result/evaluation/comparison.png
 ```
 
-For synthetic data, evaluation outputs only confirm that the evaluation and visualization pipeline can run successfully. They are not reported as model performance. For real data, the metrics represent supervised semantic segmentation performance on the user-provided test set and are not equivalent to the complete 21.5-million-sample pretraining evaluation in the paper.
-
 # Official OneScience Resources
 
 | Platform | OneScience Main Repository | Skills Repository |
@@ -173,5 +167,6 @@ For synthetic data, evaluation outputs only confirm that the evaluation and visu
 
 # Citation and License
 
-- This repository is a reproduction of the original SkySense paper.
-- The reproduction code is released under Apache License 2.0. The original datasets and official model weights remain subject to their respective source licenses.
+This repository is a reproduction of the original SkySense paper.
+
+Use of the code and data in this repository remains subject to the licenses and terms of use of their respective projects.