AI News, How to use Machine Learning and Quilt to Identify Buildings in Satellite Images

How to use Machine Learning and Quilt to Identify Buildings in Satellite Images

Recently there has been interest in using satellite images as investing tools.

Hedge funds are looking at construction in Beijing to bet on concrete demand, or they are counting cars in Walmart parking lots to get an early estimates on profits.

To access the image containing light with wavelengths 0.630–0.680 µm (red band): More information about the available image bands is here.

This image was used to sharpen the red, green, and blue bands (r,g,b) by replacing the intensity in each band with the panchromatic image.

NDVI = (NIR-Red)/(NIR+Red) This is a well know feature in the remote sensing community and was suggested by a colleague (one of the nice parts of the Insight Program is the diverse background of the Fellows).

The second feature I call a “building finder” is designed to find edges in the image, and is known in image processing lingo as edge detection.

This provided the resources, more specifically the memory to quickly explore the data and trained my models at least 20 times faster than my laptop.

This actually was not the best metric for this analysis, where the recall of buildings provides the best measure of the measure of the rate of urbanization.

would love to re-optimize with TPOT using my preferred metric, but because of time constraints, I did an ad-hoc optimization and settled for an XGBoost classifier with 350 estimators.

Taking the classifier outputs from the 2014 image and 2017 image (shown above), I apply a gaussian filter to smooth the expectation and subtract.

However the model is also sensitive to color or exposure variation that is not consistent with new construction (the red area in the lower portion of the two images).

The example mentioned in the Modeling section was trained on 10% of the data, but still achieved 83% accuracy (total) and 58% recall (buildings) across the entire image.

While the time series analysis will require cross image training or perhaps more feature tuning, the per image performance is reasonable.

A more careful preparation of the labels and training several images is the next obvious step, but due to time constraints this wasn’t possible at this time.

Introduction to Pseudo-Labelling : A Semi-Supervised learning technique

We have made huge progress in solving Supervised machine learning problems.

A human brain does not require millions of data for training with multiple iterations of going through the same image for understanding a topic.

Can we build a system capable of requiring minimal amount of supervision which can learn majority of the tasks on its own.

To solve this purpose, we find a simple solution that we download some images from the web to increase our training data.

After running supervised algorithm on this data, our model will definitely out-perform the model just containing two images in the training data.

But this approach is only valid for small purposes because human annotation to a large dataset can be very hard and expensive.

So, to solve these type of problems, we define a different type of learning known as semi-supervised learning, which is used both labelled data (supervised learning) and unlabelled data (unsupervised learning).

If we notice the difference between the above two images, you can say that after adding unlabelled data, the decision boundary of our model has become more accurate.

In this technique, instead of manually labeling the unlabelled data, we give approximate labels on the basis of the labelled data.

Now, let’s read train and test file that we have downloaded and do some basic preprocessing in order to form modelling.

If you have notice sample_rate was one of the parameter, which denotes the percentage of unlabelled data to be used as the pseudo labelled for the modelling purpose.

In this paper, not only images are used for modelling but the keywords associated with labelled and unlabelled images are also used to improve the classifier using semi-supervised learning.

Source: link Human trafficking is one of the most atrocious crimes and among the challenging problems facing law enforcement which demands attention of global magnitude.

Semi-supervised learning is to applied to use both labelled and unlabelled data in order to produce better results than the normal approaches.

Therefore, try to explore it further and learn other types of semi-supervised learning technique and share with the community in the comment section.

YOLO: Real-Time Object Detection

You only look once (YOLO) is a system for detecting objects on the Pascal VOC 2012 dataset.

It can detect the 20 Pascal object classes: YOLO is joint work with Santosh, Ross, and Ali, and is described in detail in our paper.

This network divides the image into regions and predicts bounding boxes and probabilities for each region.

It looks at the whole image at test time so its predictions are informed by global context in the image.

It also makes predictions with a single network evaluation unlike systems like R-CNN which require thousands for a single image.

Now you can run the Darknet yolo command in testing mode: I've included some example images to try in case you need inspiration.

Assuming your weight file is in the base directory, you will see something like this: Darknet prints out the objects it detected, its confidence, and how long it took to find them.

Instead of supplying an image on the command line, you can leave it blank to try multiple images in a row.

Instead you will see a prompt when the config and weights are done loading: Enter an image path like data/eagle.jpg to have it predict boxes for that image.

If you have a smaller graphics card you can try using the smaller version of the YOLO model, yolo-small.cfg.

The small version of YOLO only uses 1.1 GB of GPU memory so it should be suitable for many smaller graphics cards.

Once you get the file 2012test.tar you need to run the following commands: These commands extract the data and generate a list of the full paths of the test images.

The numbers won't match exactly since I accidentally deleted the original weight file but they will be approximately the same.

Then run the command: YOLO will display the current FPS and predicted classes as well as the image with bounding boxes drawn on top of it.

To get all the data, make a directory to store it all and from that directory run: There will now be a VOCdevkit/ subdirectory with all the VOC training data in it.

Darknet wants a .txt file for each image with a line for each ground truth object in the image that looks like: Where x, y, width, and height are relative to the image's width and height.

In your directory you should see: The text files like 2007_train.txt list the image files for that year and image set.

Edit src/yolo.c, lines 54 and 55: train_images should point to the train.txt file you just generated and backup_directory should point to a directory where you want to store backup weights files during training.

If you want to generate the pre-trained weights yourself, download the pretrained Extraction model and run the following command: But if you just download the weights file it's way easier.

If you want it to go faster and spit out fewer numbers you should stop training and change the config file a little.

A Definitive Guide To Build Training Data for Computer Vision7 min read

The AI effect has influenced the product roadmaps of all enterprise companies which now have prominent AI-based applications getting launched each quarter to automate their business processes.

If you’re just getting started, There are some great free and paid standard datasets: Existing Open Labeled Dataset Repositories: These datasets serve as a good starting point for anyone looking to get started with learning ML.

There are primarily 2 things you need to be concerned about here: Note: The data for the use cases mentioned above is usually images, videos or even 3D point clouds in case of LIDAR equipped cars.

Another list of Image Annotation tools (Free to use): Building Custom Annotation Tools from scratch If open tools don’t fit your needs, you might have to put in engineering resources to customise them or even build something from scratch.

Outsource your Image annotation needs Companies like Playment build special tools which incorporate the best practices learned from annotating thousands of images every day across a variety of scenarios &

You’ll need to hire a BPO who understand AI, onboard them onto your tool, train them on annotation best practices, build more tools to view their work, build QA models to ensure labeling accuracy, ensure they’re not cutting slack &

We are going through MTurk at the moment but our team needs something simpler” No outsourcing firm or agent can solve scale for 100,000 image annotations in a small amount of time.

But traditional crowdsourcing platforms like Amazon Mechanical Turk is mere a microtasks freelancing marketplace where all the effort of task creation, worker incentivization, QA is the task creator.

From determining the crowd capacity, creating workflows to handling task design, instructions, qualifying/managing/paying annotators, and QA, this approach requires the least amount of effort from the customer (by far).

With guaranteed enterprise SLAs,  you get better quality than in-house annotator with the scale and speed of crowdsourcing, minus the time and effort at your end.

In such situation, it is not very smart and efficient to load every single image from the hard seperately and apply image preprocessing and then pass it

Despite the required time to apply the preprocessing, it's way more time consuming to read multiple images from a harddrive than

In this post we learn how to save a large number of images in a single HDF5 file and then load them from the file in batch-wise manner.

We give each cat image a label = 0 and each dog image a label = 1.

We also divide the data set into three train (%60), validation (%20), and test parts (%20).

To store images, we should define an array for each of train, validation and test sets with the shape of (number of data, image_height, image_width, image_depth) in Tensorflow order or (number of data, image_height, image_width, image_depth) in Theano order.

we calculate the pixel-wise mean of the train set and save it in an array with the shape of (1, image_height, image_width, image_depth). Note

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