diff --git a/DATA_SuPER/DataV_SuPER.py b/Basemap_SuPER/DataV_SuPER.py
similarity index 96%
rename from DATA_SuPER/DataV_SuPER.py
rename to Basemap_SuPER/DataV_SuPER.py
index ccaa2db..c2d1e1d 100644
--- a/DATA_SuPER/DataV_SuPER.py
+++ b/Basemap_SuPER/DataV_SuPER.py
@@ -10,7 +10,7 @@
 
 -------------------------------------------------------------------------------                        
 Authors: Hong Xie
-Last Updated: 2025-10-14
+Last Updated: 2025-10-20
 ===============================================================================
 """
 
@@ -32,7 +32,7 @@ def get_datav_json(accode: str) -> dict:
     return response.json()
 
 
-def fetch_and_save_geojson(accode: str, city_name: str, out_dir: str) -> Path:
+def fetch_and_save_geojson(accode: str, city_name: str, out_dir: Path) -> Path:
     """
     获取 DataV 原始数据, 先保存为 .json; 随后清洗属性并另存为 .geojson.
     """
@@ -190,7 +190,7 @@ def resolve_adcode_by_name(city_name: str, prefer_full: bool = False) -> Optiona
     return None
 
 
-def fetch_and_save_geojson_by_name(city_name: str, out_dir: str, prefer_full: bool = False) -> Path:
+def fetch_and_save_geojson_by_name(city_name: str, out_dir: Path, prefer_full: bool = False) -> Path:
     """
     通过城市名称解析 adcode, 并直接拉取与保存 GeoJSON.
     """
@@ -201,10 +201,11 @@ def fetch_and_save_geojson_by_name(city_name: str, out_dir: str, prefer_full: bo
 
 
 if __name__ == "__main__":
+    # city_name = "湖北省"
     # city_name = "武汉市"
     # city_name = "十堰市"
-    # city_name = "湖北省"
-    city_name = "钟祥市"
-    out_dir = "./data/vectors/"
+    # city_name = "钟祥市"
+    # city_name = ""
+    out_dir = Path("./data/vectors/")
     out = fetch_and_save_geojson_by_name(city_name, out_dir, prefer_full=False)
-    print(f"Saved raw JSON and GeoJSON for {city_name}: {out}")
+    print(f"Saved raw JSON and GeoJSON for {city_name}: {out}.")
diff --git a/DATA_SuPER/DEM_SuPER.py b/DATA_SuPER/DEM_SuPER.py
index 0018783..e743156 100644
--- a/DATA_SuPER/DEM_SuPER.py
+++ b/DATA_SuPER/DEM_SuPER.py
@@ -115,7 +115,7 @@ def download_granule(granule_urls: list[str], output_dir: str) -> bool:
         except Exception as e:
             # 下载失败时, 先尝试使用 requests 库下载
             for url in granule_urls:
-                response = requests.get(url)
+                response = requests.get(url, timeout=30)
                 if response.status_code == 200:
                     with open(
                         os.path.join(output_dir, os.path.basename(url)), "wb"
@@ -126,8 +126,6 @@ def download_granule(granule_urls: list[str], output_dir: str) -> bool:
                         f"Error downloading data: {response.status_code}. Skipping."
                     )
                     return False
-            logging.error(f"Error downloading data: {e}. Skipping.")
-            return False
     logging.info("All Data already downloaded.")
     return True
 
diff --git a/DATA_SuPER/S1_SAR_SuPER.py b/DATA_SuPER/S1_SAR_SuPER.py
index f9e4311..4ec4534 100644
--- a/DATA_SuPER/S1_SAR_SuPER.py
+++ b/DATA_SuPER/S1_SAR_SuPER.py
@@ -19,7 +19,7 @@ RTC-S1 数据产品由美国宇航局喷气推进实验室 (JPL) OPERA 项目组
     5. 地形校正 (Terrain Flattening)
     6. UTM投影重采样
 - 产品特性:
-    - 时间覆盖: 2021-01-01起 (持续更新)
+    - 时间覆盖: 2014-01-01起 (历史数据已全覆盖, 并持续更新最新数据)
     - 空间分辨率: 30米
     - 数据格式: GeoTIFF/HDF5
 - 进一步预处理: