adding sample notebook for extracting slums from satellite imagery and detecting brick kilns sample. #506

sandeepgadhwal · 2019-09-11T18:26:57Z

Fixes #2457 arcgis.learn - Slum Change Detection notebook sample.

added sample notebook for extracting slums from satellite imagery.

review-notebook-app · 2019-09-11T18:27:02Z

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AtmaMani · 2019-09-17T23:54:56Z

@cyber-aman could you do the first round of reviews?

review-notebook-app · 2019-09-19T09:33:32Z

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cyber-aman commented on 2019-09-19T09:33:32Z
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{review note} Replace introduction with ????

With Growing urbanization demand for bricks is increasing almost universally. Brick production is a very large and traditional industry in many parts of Asia. The brick sector in India, although unorganized, is tremendous in size. India is the second largest brick producer (China dominates with 54% share) in the world [1].

Brick Kiln cause pollution by emitting toxic elements that contain particulate matters rich in rich in carbon particles and high concentration of carbon monoxides and oxides of sulphur (SOx). These are extremely hazardous to health can impact physical and mental growth of children.

There are several guidelines and directives issued by Govt. of India in order to control unplanned expansion and pollution. Central Pollution Control Board (CPCB) issued a directive in June 2017 which asked brick kilns across India to convert to zigzag setting with rectangular kiln shape. This directive clearly stated that brick kilns operating without permission and consent from respective SPCBs would be shut down [2]. Despite the directive, there are many brick kilns operating without following the design norms prescribed by CPCB.

In this study, we will use Deep Learning techniques integrated with ArcGIS tools to detect brick kilns around Delhi NCR area in India that are not built based on directions from CPCB. Deep Learning is a tried and tested method for object detection within images. We will use these techniques to detect brick kilns based on design specified by CPCB. High level steps that we will follow are:

Collect Data using ArcGIS Pro
Train Deep Learning model using arcgis.learn
Deploy the trained model using ArcGIS Pro

[1] Sheikh, Afeefa. (2014). Brick kilns: Cause of Atmospheric Pollution.

[2] https://www.cseindia.org/content/downloadreports/9387

review-notebook-app · 2019-09-19T09:33:33Z

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cyber-aman commented on 2019-09-19T09:33:33Z
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A section is needed to explain what data are we using and how a brick kiln looks like in the data we are using for our model.

sandeepgadhwal commented on 2019-09-20T04:50:21Z
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above the image i have written

We will be using ESRI World Imagery Basemap layer to train the model, and for a comparative analysis we will also be using a ESRI World Imagery Basemap layer from year 2014 which we found using the wayback imagery tool.

and below the image

On the above image we can see Zig-Zag shaped brick kilns (Blue Marker) and oval shaped brick kilns (Red Marker).

review-notebook-app · 2019-09-19T09:33:34Z

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cyber-aman commented on 2019-09-19T09:33:34Z
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We will use Export Training Data For Deep Learning tool available in ArcGIS Pro (same tool is available in ArcGIS Enterprise as well).

mention values to be passed and ask users to follow tool reference page

Input Raster : world_imagery_2019

Output Folder :

.....

review-notebook-app · 2019-09-19T09:33:35Z

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cyber-aman commented on 2019-09-19T09:33:34Z
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Can we mention that "after filling all details and running the Export Training Data For Deep Learning tool, a code like above will be generated that will create all the necessary files...........

What are the necessary files? can these be explained?

sandeepgadhwal commented on 2019-09-20T05:02:37Z
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Should we list out what we export it will be too much information for users.

review-notebook-app · 2019-09-19T09:33:35Z

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cyber-aman commented on 2019-09-19T09:33:35Z
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We will train our model using arcgis.learn module within ArcGIS API for Python. arcgis.learn contains tools and deep learning capabilities required for this study. A detailed documentation to install and setup the environment is available here.

review-notebook-app · 2019-09-19T09:33:36Z

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cyber-aman commented on 2019-09-19T09:33:36Z
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Change required > Necessary Imports

review-notebook-app · 2019-09-19T09:33:37Z

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cyber-aman commented on 2019-09-19T09:33:36Z
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** Remove prepare data heading from the list of contents**

We will now use the prepare_data() function within arcgis.learn to apply transformations and augmentations on the training data. <explain what transformation will be done and why>. prepare_data() takes 3 parameters :

...

review-notebook-app · 2019-09-19T09:33:38Z

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cyber-aman commented on 2019-09-19T09:33:37Z
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Visualize Training Data

To make sense of training data we will use the show_batch() method in arcgis.learn.show_batch() randomly picks few samples from the training data and visualizes those with the same symbology as in ArcGIS Pro.

review-notebook-app · 2019-09-19T09:33:39Z

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cyber-aman commented on 2019-09-19T09:33:38Z
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Explain above visualization a bit...

review-notebook-app · 2019-09-19T09:33:39Z

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cyber-aman commented on 2019-09-19T09:33:39Z
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Load Model Architecture

Introduce SingleShotDetector model architecture as in this notebook. add references as appropriate

review-notebook-app · 2019-09-19T09:33:40Z

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cyber-aman commented on 2019-09-19T09:33:40Z
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Train The Model

** 'keep Find an Optimal Learning Rate' and 'Fit the model' as the subheadings under the main heading i.e., Train The Model**

Find an Optimal Learning Rate

Deep learning models are optimized by tuning these hyperparameters. A hyperparameter is a parameter whose value is set before the learning process begins [3]. Learning rate is a key parameter that determines how we adjust weights for our network with respect to loss gradient [4]. Too high learning rate will lead to the convergence of our model to an sub-optimal solution and too low learning can slow down the convergence of our model. We can use the lr_find() method to find an optimum learning rate at which can train a robust model fast enough.

[3] https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)

[4] https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10

review-notebook-app · 2019-09-19T09:33:41Z

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cyber-aman commented on 2019-09-19T09:33:40Z
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Based on the learning rate plot above, we can see that the learning rate suggested by lr_find() for our training data is 1e-03

review-notebook-app · 2019-09-19T09:33:41Z

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cyber-aman commented on 2019-09-19T09:33:41Z
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** indent heading Fit the Model under Train the Model as in previous section **

Epoch defines how many times model is exposed to entire training set. To start, we will use 10 epochs to train our model.

review-notebook-app · 2019-09-19T09:33:42Z

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cyber-aman commented on 2019-09-19T09:33:42Z
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Training data is split into training set and validation set at the Prepare Data step. By default training vs validation set proportion is 80% & 20%. Output of fit() method gives us loss on training and validation sets. This helps us to assess the generalization capability of the model on unseen data and prevent overfitting. Here, with only 10 epochs, we are see reasonable results. Further improvement can be achieved through more sophisticated hyperparameter tuning.

Next step is to save the model for further training or inference later. By default, model will be saved into data path specified in the beginning of this notebook.

review-notebook-app · 2019-09-19T09:33:43Z

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cyber-aman commented on 2019-09-19T09:33:43Z
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remove helpind words like 'just'.

why mention unetclassifier here?

review-notebook-app · 2019-09-19T09:33:43Z

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cyber-aman commented on 2019-09-19T09:33:43Z
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give a proper name to saved model. eg. ssd_resnet_brick-kiln_01

review-notebook-app · 2019-09-19T09:33:44Z

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cyber-aman commented on 2019-09-19T09:33:44Z
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To retrain a saved model (eg. on new training data), we can load it again using the code below and follow the steps as mentioned in Fit the model step

review-notebook-app · 2019-09-19T09:33:45Z

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cyber-aman commented on 2019-09-19T09:33:45Z
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Visualize results in validation set

Its a good practice to see results of the model viz-a-viz Ground Truth. Code below picks a random samples and shows us ground truth and model predictions, side by side. This enables us to preview the results of the model within the notebook.

review-notebook-app · 2019-09-19T09:33:46Z

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cyber-aman commented on 2019-09-19T09:33:46Z
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review-notebook-app · 2019-09-19T09:33:46Z

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cyber-aman commented on 2019-09-19T09:33:46Z
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We will use saved model to extract classified raster using Detect Objects Using Deep Learning tool available in both ArcGIS Pro and ArcGIS Enterprise. For this sample we will use ESRI World Imagery basemap and for a comparative analysis we will also detect brick kilns on an older ESRI world imagery layer from year 2014 within wayback imagery tool.

for GP tool parameters, just mention values to be passed and ask users to follow tool reference page

Input Raster: Map2014
Output Detected Objects: brick_kilns_prediction_2014
Model Definition:
padding:
threshold:
nms_overlap:
batch_size
exclude_pad_detection
Cell Size:
Processor Type:
Parallel Processing Factor:

review-notebook-app · 2019-09-19T09:33:47Z

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cyber-aman commented on 2019-09-19T09:33:47Z
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no need for heading Final Output

need to refine output...

Detect Objects Using Deep Learning returns a feature class that can be further refined using the Definition query and Non Maximum Suppression tools. This can also be shared as hosted feature layer in your Portal.

review-notebook-app · 2019-09-19T09:33:48Z

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cyber-aman commented on 2019-09-19T09:33:48Z
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need to expand conclusion and tie to the introduction where we define problem

sandeepgadhwal commented on 2019-09-20T06:17:47Z
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In the cell below it we have added an example of dashboard that can be made using the output.

review-notebook-app · 2019-09-19T09:33:49Z

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cyber-aman commented on 2019-09-19T09:33:48Z
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need this image????

sandeepgadhwal commented on 2019-09-20T06:15:38Z
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Here we are showing our end product from the sample.

rohitgeo · 2019-09-25T16:37:46Z

@sandeepgadhwal the dashboard is not loading fully (and it was private):

sandeepgadhwal · 2019-09-25T17:57:52Z

@sandeepgadhwal the dashboard is not loading fully (and it was private):

I cloned the dashboard but some layers and functionalities failed, I will try to publish here again.

AtmaMani · 2019-10-08T18:59:15Z

@sandeepgadhwal could you post a comment that its ready for review after you have reviewed and incorporated @cyber-aman's comments and the dashboard?

AtmaMani · 2019-11-14T22:54:08Z

@sandeepgadhwal is this ready for review?

sandeepgadhwal · 2019-11-18T09:19:32Z

@AtmaMani this can be reviewed now.

review-notebook-app · 2019-12-02T18:39:44Z

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AtmaMani commented on 2019-12-02T18:39:43Z
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The validation loss seems quite high. I wonder if we should improve this further. Or we need to justify why the loss is high and why it is acceptable.

AtmaMani · 2019-12-02T18:40:47Z

@sandeepgadhwal @cyber-aman Thanks for the sample and detailed reviews. It looks good except the brick klin sample has a high validation loss at 10 epochs. Can we improve it?

sandeepgadhwal · 2019-12-05T03:22:28Z

@AtmaMani the validation loss is going down continuously, it means the model is getting trained. We cannot compare the training loss and validation loss directly.

AtmaMani · 2019-12-09T22:49:55Z

@sandeepgadhwal agreed. I mistook validation loss as f1 score.

AtmaMani

Looks good, thanks for the reviews and continuous improvement.

adding sample notebook for extracting slums from satellite imagery

652af07

Sandeep Kumar added 2 commits September 12, 2019 15:22

incorporating changes in slums sample notebook

f071cc0

incorporating changes

285dfee

sandeepgadhwal changed the title ~~adding sample notebook for extracting slums from satellite imagery~~ adding sample notebook for extracting slums from satellite imagery and detecting brick kilns sample. Sep 14, 2019

Sandeep Kumar added 2 commits September 15, 2019 00:02

adding brick kilns sample notebook

c7312f3

incorporating changes brick kilns

b8f712f

AtmaMani requested a review from cyber-aman September 17, 2019 23:54

Sandeep Kumar added 2 commits September 20, 2019 11:51

incorporating changes suggested by amandeep

4c2acae

incorporating changes

797b939

improved results of brick kilns

322ad99

improved results

dee9604

AtmaMani assigned sandeepgadhwal Dec 2, 2019

Sandeep Kumar added 3 commits December 4, 2019 15:38

rename slums notebook to lowercase

10715e9

incorporated_changes_suggested_by_rohit

6fe9069

changes suggested by priyanka

4471d52

AtmaMani approved these changes Dec 9, 2019

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AtmaMani merged commit 99f4397 into Esri:master Dec 9, 2019

adding sample notebook for extracting slums from satellite imagery and detecting brick kilns sample. #506

adding sample notebook for extracting slums from satellite imagery and detecting brick kilns sample. #506

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