MADANI

Mapping and Data Assesment for Natural Incident

Madani is an application that makes it easier for volunteers to carry out disaster mitigation by classifying routes and impact buildings based on post-disaster satellite images.

From many disasters that have occurred in Indonesia, the problem that often occurs in the disaster mitigation process is the difficulties of determining the areas and buildings affected by the disaster, and determining the logistics distribution channels needed after a disaster occurs. Manual monitoring can be done, but it will require more resources such as human and financial resources.

Disaster mitigation follow the processes included in the standard operational procedures according to their level of disaster. MADANI is intended to support one of these processes, mainly in estimating areas and buildings affected by disasters, as well as in determining post-disaster logistics distribution channels.

Mobile Development

Feature

• Onboarding Screen

• Login

• Sign Up

• Building Damage Detection Mapping

• Road Extraction Mapping

• Central Announcement

• Information On The Latest Situation In The Post-Disaster Area

Application Flow

Prototype Application Using Adobe XD

Built With

• Adobe XD
• Android Studio Native
• Retrofit 2
• Kotlin
• Firebase Storage
• Firebase Firestore
• REST API MADANI

Machine Learning

Overview

We create a machine learning model that can classify building damage and detect road in areas affected by natural disasters.

Building Damage Classification

Dataset

We use xBD dataset, a dataset for assessing building damage from satellite imagery. xBD provides pre and post-event satellite imagery from a variety of disaster events with building polygons, classification labels for damage types, labels of damage level, and correspoinding satellite medatada. Detailed information can be found at xView2 site. We use the Palu 2018 tsunami disaster satellite data provided by xBD which can be downloaded from here. This dataset contain 226 pre and post disaster satellite imagery, with 16,764 building polygons.

Data Preparation

Crop Building Image

The image that will be used for training is a polygon image that has been extracted from a complete satellite image. After do some extraction process, then we generate a csv file with polygon ID and it's corresponding damage labels. We will use this csv later to input data into the model using flow_from_dataframe. We will apply multiple augmentations to our training data for all polygon images such as ; horizontal flip, vertical flip, rotation, width and height shift. We resize our input images to 128x128, this number is not too small to cause loss of necessary information, but not too large to cause our CNN performance slowed down.

Undersampling

There is an imbalance number of classes in the 2018 Palu tsunami dataset. We have explored the model and train the model with class weights, but because the imbalance is very extreme (there is only 1 building polygon that is labeled in class 3 out of a total of 16,764 building polygons), so we decide to reduce the classification target from 4 classes to binary classification. We apply undersampling technique to change the composition of the training dataset because the number of buildings that has no damage and the number of destroyed buildings are imbalance.

Model

We create a Keras Model with the Sequential linear stack of layers. All Convolutional blocks will use ReLU for the activation parameter, We call Flatten method to transform 3 dimensional feature maps into 1 dimensional tensor.

CNN Architecture

Model Accuracy

Road Extraction

Dataset

This dataset is provided by Road Extraction Dataset from DeepGlobe Challenge. DeepGlobe Challenge provided us with images that paired with labels. The image itself is a satellite imagery shot of regions that contain road in it. And the label mask is a grayscale image, with white standing for road pixel, and black standing for background.

The images that we used to train our model is satellite imagery shot of regions that contain road in it. And the label mask is a grayscale image, with white standing for road pixel, and black standing for background. This images we used only from the training folder from you can see in the DeepGlobe Challenge dataset and from approximately 6000 images and labels later split it into validation dataset in training model.

Data Preparation

We create our own data generator for the road extraction model as we can see in the 03 Road Extraction - Data Preparation & Exploration.ipynb. First, we created two empty numpy arrays for each images and labels using numpy.zeros, then we convert the satellite images and labels into arrays using OpenCV and append them into the created numpy arrays. This will make each empty numpy arrays filled with images and labels arrays.

Image Arrays

Labels Arrays

We will not use anymore modification (yet) such as augmentation because we need to process the images and the labels as it is to mantain the preciseness.

Model

ResUnet Architecture

Model Accuracy

Cloud Computing

Designing architecture for cloud system and build the environment to store the resource and provide server to do the process in google cloud service. Then create a database with MySQL for organizes, integrates, and control the output from the process in server and create Rest Api with Laravel framework to simplifying communication between database and android to serve the data output

Service

• Google Storage

Google storage is used to store all resource data either input and output. We create 2 bucket and using the url to call the object from storage.

• AI Platform Notebook

AI Platform Notebook is used as a server for running the machine learning process. It will call the model and doing image identification process with ML program.

• Compute Instances

Compute Instance is used to run MADANI server application. We used two instances for api server and for machine learning program.

• MySql

We used MySql database to organize and control the output from machine learning process.

• Framework Laravel We used Laravel to build rest API service to connect database and android as a client. It's a comprehensive framework and logically structured.

Process Production

1. Designing architecture cloud

   • GCP Architecture

     

2. Setup Environment

   • Create Bucket

     

   • Create VM Instances for API

     

   • Create Notebook AI

     

3. Create Database

   • DB Eaver

     

   • Database Schema

     

4. Build Rest API

   • Models

     

   • Controller

     

   • api.php

     

   • POSTMAN

     

   • API Server

     

5. Migrating all resource to cloud