Fixed more tags and added a few more datasets

Author: Greg Cohen

datasets/ADQOGS-SSA.md

@@ -0,0 +1,70 @@
+---
+{
+    "name": "ADQOGS-SSA",
+    "aliases": [],
+    "year": 2025,
+    "modalities": [
+        "Vision"
+    ],
+    "sensors": [
+        "DAVIS346",
+        "DVXplorer"
+    ],
+    "other_sensors": [],
+    "category": "Domain Specific Application",
+    "tags": [
+        "Space Datasets",
+        "Space Situational Awareness"
+    ],
+    "description": "Space Situational Awareness Dataset",
+    "dataset_properties": {
+        "available_online": true,
+        "has_real_data": true,
+        "has_simulated_data": false,
+        "has_ground_truth": true,
+        "has_frames": true,
+        "has_biases": false,
+        "distribution_methods": [
+            "Other"
+        ],
+        "file_formats": [
+            "HDF5"
+        ],
+        "availability_comment": "",
+        "dataset_links": [
+            {
+                "name": "Science Data Bank",
+                "url": "https://www.scidb.cn/en/detail?dataSetId=eb88043fa0124cdcb0f2b44ee0ec274c#p4",
+                "format": "HDF5",
+                "available": true
+            }
+        ],
+        "size_gb": 11.33,
+        "size_type": "Compressed"
+    },
+    "paper": {
+        "title": "",
+        "doi": "",
+        "authors": [],
+        "abstract": "",
+        "open_access": false
+    },
+    "citation_counts": [],
+    "links": [
+        {
+            "type": "preprint",
+            "url": "https://conference.sdo.esoc.esa.int/proceedings/sdc9/paper/197/SDC9-paper197.pdf"
+        },
+        {
+            "type": "github_page",
+            "url": "https://github.com/HazemElrefaei/EBSSA_DataAnalysis"
+        }
+    ],
+    "full_name": "Neuromorphic Vision-Based Dataset for Space Situational Awareness Applications",
+    "additional_metadata": {}
+}
+---
+
+# Dataset Description
+
+This dataset consists of labeled data for real-time detection and tracking of Resident Space Objects (RSOs). The data is acquired using two neuromorphic sensors DAVIS346 and DVXplorer640 integrated with a high-precision 0.8 m aperture Ritchey–Chrétien telescope. The dataset includes event-based recordings of 15 satellites and space debris, along with stars of varying brightness for performance benchmarking. The data is annotated to support supervised training of deep learning models. This dataset is a foundation for developing high-performance RSO detection AI algorithms, such as flash attention-based networks.

datasets/DSEC-MOT.md

@@ -14,7 +14,7 @@
     "tags": [
         "Object Tracking",
         "Multiple Object Tracking",
-        "Annotated"
+        "Annotated Datasets"
     ],
     "description": "Multiple Object Tracking",
     "dataset_properties": {

datasets/DVS-PedX.md

@@ -13,7 +13,7 @@
     "other_sensors": [],
     "category": "Human-centric Recordings",
     "tags": [
-        "Pedestrial Detection",
+        "Pedestrian Detection",
         "Crossing Intention Analysis"
     ],
     "description": "Pedestrian detection and crossing-intention analysis",

datasets/EBSC.md

@@ -0,0 +1,65 @@
+---
+{
+    "name": "EBSC",
+    "aliases": [],
+    "year": 2025,
+    "modalities": [
+        "Vision"
+    ],
+    "sensors": [
+        "Unknown"
+    ],
+    "other_sensors": [],
+    "category": "Robotic and Moving Vehicle Datasets",
+    "tags": [
+        "Moon/Mars Datasets",
+        "Space Datasets"
+    ],
+    "description": "Soil Characterization and Slip Estimation Dataset",
+    "dataset_properties": {
+        "available_online": true,
+        "has_real_data": true,
+        "has_simulated_data": false,
+        "has_ground_truth": true,
+        "has_frames": true,
+        "has_biases": false,
+        "distribution_methods": [
+            "Other"
+        ],
+        "file_formats": [
+            "HDF5"
+        ],
+        "availability_comment": "",
+        "dataset_links": [
+            {
+                "name": "Science Data Bank",
+                "url": "https://www.scidb.cn/en/detail?dataSetId=49217e0669b6490a8701141279fbaa9d#p5",
+                "format": "HDF5",
+                "available": true
+            }
+        ],
+        "size_gb": 48.2,
+        "size_type": "Compressed"
+    },
+    "paper": {
+        "title": "",
+        "doi": "",
+        "authors": [],
+        "abstract": "",
+        "open_access": false
+    },
+    "citation_counts": [],
+    "links": [
+        {
+            "type": "github_page",
+            "url": "https://github.com/HazemElrefaei/EBSC_DataAnalysis/tree/master"
+        }
+    ],
+    "full_name": "",
+    "additional_metadata": {}
+}
+---
+
+# Dataset Description
+
+A neuromorphic vision-based dataset is presented that will significantly automate two fundamental navigation tasks such as soil characterization and slip estimation. The dataset comprises 270 experiments collected over five different speeds, two lighting conditions, three soil types and two failure scenarios, capturing wheel-terrain interactions. The dataset is a benchmark for event-based perception and facilitates reproducibility through custom models that can be trained and validated on the open-source data for real-time soil characterization and slip estimation.