updating readme

README.md

@@ -11,94 +11,7 @@ which is an AI-Based pipeline for city-scale building information modeling (BIM)
 
 ## How to use?
 
-###### Tested with Tensorflow in Python 3.6.6 
-
-### 1. Data preparation 
-
-
-[Prepare data](src/preparedata/README.md)
-
-
-### 2. Train
-
-##### Roof
-###### training
-```
-cd src/training/roof/2_train
-# train (better to run on a GPU machine)
-sh finetune_inception_v3_on_roof_train.sh
-```
-
-The training takes a long time on laptops. 
-If you don't want to run the training process, we have a CNN trained on TACC and can be downloaded [here](https://berkeley.box.com/shared/static/awyyc22sjwknn9xg3p7wru4v5zwnlkjp.zip).
-Put the downloaded file inside src/training/roof/tmp/roof-traindir/ and unzip it.
-
-###### testing
-
- Before your run the test *"sh finetune_inception_v3_on_roof_eval.sh"*, make sure you set the following variables correctly in *"finetune_inception_v3_on_roof_eval.sh"*:
- ```
- checkpoint_file = the-path-of-your-checkpoint-file
- TEST_DIR = the-path-of-your-testing-images-dir
- ```
- We've prepared some labeld images for you to test. These images can be downloaded [here](https://berkeley.box.com/shared/static/wfwf4ku9561lcytldy1p7vkjoobgv9sz.zip).
-
-Now you can test if the trained CNN works or not:
- ```
-cd src/training/roof/2_train
-sh finetune_inception_v3_on_roof_eval.sh
-```
-
-
-### 3. Predict
-
-##### Roof
-Now we use the CNN to predict roof types based on satellite images.
-
-Firstly we need to download those images by calling Google API (will cost $).
-
-```
-cd src/predicting
-python downloadRoofImages.py
-```
-
-To save $, instead of running the above python, you can just download them 
-[here](https://berkeley.box.com/shared/static/n8l9kusi9eszsnnkefq37fofz22680t2.zip).
-
-
-Now we can make predictions:
-
-```
-cd src/predicting
-sh classifyRoof.sh
-```
-This script will look into the BIM file and call CNN to predict the roof type of a building if the image is downloaded.
-
-If the image is not downloaded, it will assign a null value for the roof type in the new BIM file.
-
-
-### 4. Enhance
-
-Use [*SURF*](https://github.com/charlesxwang/SURF) to predict missing building information:
-
-
-##### Year built
-
-<img src="docs/images/yearBuilt-prediction-error.png" width="700">
-
-
-
-##### Number of stories 
-
-<img src="docs/images/stories_Predictions_classification_error.png" width="700">
-
-##### Structure type
-
-<img src="docs/images/structureType_Predictions_classification_error.png" width="700">
-
-##### Occupancy
-
-<img src="docs/images/occupancy_Predictions_classification_error.png" width="700">
-
+Read the document <a href="https://nheri-simcenter.github.io/BRAILS/">here</a>.
 
 ## Release of BIM data
 SimCenter will post obtained data here.