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This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning e. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities.

If you find this work useful please give it a star and consider sponsoring it. You can also follow me on Twitter and LinkedIn where I aim to post frequent updates on my new discoveries, and I have created a dedicated group on LinkedIn. I have also started a blog here and have published a post on the history of this repository called Dissecting the satellite-image-deep-learning repo If you use this work in your research please cite using the citation information on the right.

This section explores the different deep and machine learning ML techniques applied to common problems in satellite imagery analysis. Good background reading is Deep learning in remote sensing applications: A meta-analysis and review.

The classic cats vs dogs image classification task, which in the remote sensing domain is used to assign a label to an image, e.

The more complex case is applying multiple labels to an image. This approach of image level classification is not to be confused with pixel-level classification which is called semantic segmentation. In general, aerial images cover large geographical areas that include multiple classes of land, so treating this is as a classification problem is less common than using semantic segmentation.

I recommend to get started with the EuroSAT dataset. Segmentation will assign a class label to each pixel in an image. Segmentation is typically grouped into semantic, instance or panoptic segmentation.

In semantic segmentation objects of the same class are assigned the same label, whilst in instance segmentation each object is assigned a unique label. Panoptic segmentation combines instance and semantic predictions. Image annotation can take longer than for object detection since every pixel must be annotated. Note that many articles which refer to 'hyperspectral land classification' are actually describing semantic segmentation.

Extracting roads is challenging due to the occlusions caused by other objects and the complex traffic environment.

In instance segmentation, each individual 'instance' of a segmented area is given a unique lable. For detection of very small objects this may a good approach, but it can struggle seperating individual objects that are closely spaced. Several different techniques can be used to count the number of objects in an image. The returned data can be an object count regression , a bounding box around individual objects in an image typically using Yolo or Faster R-CNN architectures , a pixel mask for each object instance segmentation , key points for an an object such as wing tips, nose and tail of an aircraft , or simply a classification for a sliding tile over an image.

A good introduction to the challenge of performing object detection on aerial imagery is given in this paper. In summary, images are large and objects may comprise only a few pixels, easily confused with random features in background. For the same reason, object detection datasets are inherently imbalanced, since the area of background typically dominates over the area of the objects to be detected. Model accuracy falls off rapidly as image resolution degrades, so it is common for object detection to use very high resolution imagery, e.

A particular characteristic of aerial images is that objects can be oriented in any direction, so using rotated bounding boxes which aligning with the object can be crucial for extracting metrics such as the length and width of an object. When the object count, but not its shape is required, U-net can be used to treat this as an image-to-image translation problem.

A variety of techniques can be used to count animals, including object detection and instance segmentation. For convenience they are all listed here:. Oil is stored in tanks at many points between extraction and sale, and the volume of oil in storage is an important economic indicator.

Generally treated as a semantic segmentation problem or custom features created using band math. Generally speaking, change detection methods are applied to a pair of images to generate a mask of change, e.

Crop yield is very typically application and has its own section below. The goal is to predict economic activity from satellite imagery rather than conducting labour intensive ground surveys. Super-resolution attempts to enhance the resolution of an imaging system, and can be applied as a pre-processing step to improve the detection of small objects or boundaries. Its use is controversial since it can introduce artefacts at the same rate as real features.

GANS are famously used for generating synthetic data, see the section Synthetic data. Also checkout Synthetic data. This is a class of techniques which attempt to make predictions for classes with few, one or even zero examples provided during training. These approaches are particularly relevant to remote sensing, where there may be many examples of common classes, but few or even zero examples for other classes of interest.

These techniques use unlabelled datasets. The machine predicts any part of its input for any observed part, all without the use of labelled data. Supervised learning forms the icing on the cake, and reinforcement learning is the cherry on top.

However labelling at scale take significant time, expertise and resources. Active learning techniques aim to reduce the total amount of annotation that needs to be performed by selecting the most useful images to label from a large pool of unlabelled images, thus reducing the time to generate useful training datasets. These processes may be referred to as Human-in-the-Loop Machine Learning. Federated learning is a process for training models in a distributed fashion without sharing of data. Image registration is the process of registering one or more images onto another typically well georeferenced image.

Traditionally this is performed manually by identifying control points tie-points in the images, for example using QGIS. This section lists approaches which mostly aim to automate this manual process.

There is some overlap with the data fusion section but the distinction I make is that image registration is performed as a prerequisite to downstream processes which will use the registered data as an input. It can also cover fusion with non imagery data such as IOT sensor data. NeRF stands for Neural Radiance Fields and is the term used in deep learning communities to describe a model that generates views of complex 3D scenes based on a partial set of 2D images.

Processing on board a satellite allows less data to be downlinked. Other applications include cloud detection and collision avoidance. A number of metrics are common to all model types but can have slightly different meanings in contexts such as object detection , whilst other metrics are very specific to particular classes of model.

The correct choice of metric is particularly critical for imbalanced dataset problems, e. This section contains a short list of datasets relevant to deep learning, particularly those which come up regularly in the literature.

Since there is a whole community around GEE I will not reproduce it here but list very select references. The kaggle blog is an interesting read. Not satellite but airborne imagery. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared.

The training and test labels are one-hot encoded 1x6 vectors. Each image patch is size normalized to 28x28 pixels. Data in. In this challenge, you will build a model to classify cloud organization patterns from satellite images.

Training data can be hard to acquire, particularly for rare events such as change detection after disasters, or imagery of rare classes of objects. In these situations, generating synthetic training data might be the only option. This has become quite sophisticated, with 3D models being use with open source games engines such as Unreal. A GPU is required for training deep learning models but not necessarily for inferencing , and this section lists a couple of free Jupyter environments with GPU available.

There is a good overview of online Jupyter development environments on the fastai site. Also consider one of the many smaller but more specialised platorms such as paperspace. For an overview on serving deep learning models checkout Practical-Deep-Learning-on-the-Cloud. There are many options if you are happy to dedicate a server, although you may want a GPU for batch processing. For serverless use AWS lambda. A common approach to serving up deep learning model inference code is to wrap it in a rest API.

EC2 instance. Note that making this a scalable solution will require significant experience. Using lambda functions allows inference without having to configure or manage the underlying infrastructure. The model is run in the browser itself on live images, ensuring processing is always with the latest model available and removing the requirement for dedicated server side inferencing.

There are also toolkits for optimisation, in particular ONNX which is framework agnostic. MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. For supervised machine learning, you will require annotated images. For example if you are performing object detection you will need to annotate images with bounding boxes. Check that your annotation tool of choice supports large image likely geotiff files, as not all will.

Note that GeoJSON is widely used by remote sensing researchers but this annotation format is not commonly supported in general computer vision frameworks, and in practice you may have to convert the annotation format to use the data with your chosen framework.

There are both closed and open source tools for creating and converting annotation formats. Some of these tools are simply for performing annotation, whilst others add features such as dataset management and versioning. Note that self-supervised and active learning approaches might circumvent the need to perform a large scale annotation exercise.

In general cloud solutions will provide a lot of infrastructure and storage for you, as well as integration with outsourced annotators. I recommend using geojson for storing polygons, then converting these to the required format when needed.