Update README.md

README.md

@@ -9,13 +9,13 @@ license: apache-2.0
 
 <!-- Provide a quick summary of the dataset. -->
 
-We downloaded satellite images from Major-TOM, filtered for Germany, and processed them into vector embeddings.
+We downloaded satellite images from Major-TOM, provided by the European Space Agency, filtered for Europe, and used our vectorisation engine 'Synapsis' to extract vector embeddings with one of the latest embedding model.
 
 ## Datasource Details
 |               | Value                                   |
 |---------------|-----------------------------------------|
 | Datasource    | Major-TOM/Core-S2L2A                    |
-| Region        | box(5.98865807458, 47.3024876979, 15.0169958839, 54.983104153) (Covers whole of Germany) |
+| Region        | box(5.98865807458, 47.3024876979, 15.0169958839, 54.983104153) (Covers whole of Europe) |
 | Date Range    | ('2020-01-01', '2025-01-01')            |
 | Cloud Cover   | (0, 10)                                 |
 | No Data       | (0.0, 0.0)                               |
@@ -32,23 +32,27 @@ This dataset shows the relationship between our embeddings/vectors and Major TOM
 
 **Embedding.dat**
 
-This dataset has the vector embeddings calculated by us.
+This dataset entails the vector embeddings calculated by Quasara.
 
-What we did was:
+What we did on our side is:
 
-a) to vectorise the entire Major-TOM image data for Europe;
+a) download the Major-TOM dataset and filter it for images showing Europe;
 
-b) used the OPENCLIP_SIGLIP_400M on the Quasara Platform for embedding generation
+b) vectorising the entire Major-TOM image data;
 
-c) no pre-training, no labelling happened in the preparation of this dataset
+c) using the OPENCLIP_SIGLIP_400M and our scalable Vectorisation Engine 'Synapsis' for embedding extraction.
+
+There was no pre-training, labelling or finetuning happening to prepare the vector embeddings of the Major TOM dataset.
 
 ## Uses
 
+Potential use cases for the dataset we came up with is the data exploration of the data using text prompts, image prompts, unsupervised clustering of images, building a RAG or even building a chat bot on top of the vector embeddings.
+What can you come up with to build?
 <!-- Address questions around how the dataset is intended to be used. -->
 
 # MajorTOM-Europe Dataset
 
-The **MajorTOM-Europe dataset** provides embeddings derived from high-resolution satellite images of the Europe region, generated using the OpenCLIP SigLIP model. These embeddings, extracted from images covering a range of geographic coordinates across Germany, provide a powerful tool for various applications.
+The **MajorTOM-Europe dataset** provides embeddings derived from high-resolution satellite images of the Europe region, generated using the OpenCLIP SigLIP model. These embeddings, extracted from images covering a range of geographic coordinates across Europe, provide a powerful tool for various applications.
 
 ## Dataset Information
 
@@ -67,6 +71,8 @@ The MajorTOM-Europe dataset can be utilized for various applications, including:
   - Track deforestation
   - Observe urban expansion
   - Monitor water body dynamics
+  - Finding countless objects from airports, golf courses to wind farms
+
 
 - **Precision Agriculture:**
   - Analyze crop health