With the popularization of Artificial Intelligence and its gradual emergence as the core technology that is impelling momentous developments in a large number of fields, there has been a spurt in the use of machine learning and deep learning as well. As per multiple surveys and studies, AI and Machine Learning would be among the highest-paid and most lucrative career streams in the years to come.
AI and Machine Learning would revolutionize our existing technological frameworks and usher in a new industrial age by reorienting and transforming everything from the simplest of appliances to automobiles. But that’s not all! The applications of Machine Learning are not only limited to the terrestrial zone but have reached for the sky too, both literally as well as figuratively.
Just like all other domains that are constantly reimagining themselves and girding for the future, the domain of remote sensing is also undergoing profound changes and witnessing increasing use of specified algorithms when Big Data and Cloud have become almost ubiquitous.
Machine Learning algorithms have proved to be a powerful tool for analyzing satellite imagery of any resolution and proving better and more nuanced insights. In its nascent stages, there are a few challenges as well in the application of Machine Learning on satellite images, including the extraordinarily large file size of satellite imagery and data format being exclusively designed for geo-referenced images that make Big Data and Machine Learning applications quite difficult. Though, by identifying the utility and value and designing a specially crafted solution enables the development of programs to overcome these limitations.
Embrace of big players
Earth Observation giants like DigitalGlobe and Planet also extensively use Machine Learning for satellite imagery. Planet has a dedicated solution for Machine Learning called Planet Analytics, which uses Machine Learning algorithms for processing of daily satellite imagery, detecting and classifying objects, locating topographic and geographical features and consistently monitoring even the most infinitesimal change over time. The information feed is seamlessly integrated into the workflows and offers dazzling insights on almost any place on the globe.
Satellite imagery refining start-up Descartes Lab has a cloud-based platform that applies Machine Learning forecasting models to petabytes of satellite imagery that is drawn from a number of sources. Descartes Labs has an expertise applying Machine Learning to Earth Observation satellite imagery. Before machine learning can extract valuable data from imagery, the data has to be pre-processed to line up pixels and correct for varying atmospheric conditions and spectral calibrations.
Last year, Descartes Labs created a living atlas of the world using Machine Learning and Cloud. The atlas has been designed to provide real-time forecasts of commodity agriculture and provides analysis of the patterns of land usage. Machine Learning was used to predict food supplies earlier than the conventional methods. This approach would help to predict and avert food shortages.
DigitalGlobe GBDX team runs Machine Learning object detection on a very large scale. Every time a new model is applied to GBDX a comparison is made to ascertain the plus points over existing capabilities.
Google is also among the trailblazers tapping the potential of Machine Learning in satellite imagery. Google launched an application that can tell the exact geo-location of any photograph captured anywhere on earth. The project called PlaNet which deploys the power of machine learning is based on the combination of convolutional neural networks with mapping technology. The information that it provides is truly invaluable and unprecedented in both qualitative and quantitative aspects.
Multiple utilities of Machine Learning in satellite imagery
Other than better clarity and visualization, Machine Learning also lets us interpret the pic in many ways and from different vantage points. Scientist Xueqing Deng and his colleagues at the University of California created a machine-learning algorithm recently to create ground-level images by looking at aerial satellite imagery. This type of machine intelligence is called generative adversarial network which consists of two neural networks: generator and discriminator.
Images are created by the generator and then processed by the discriminator on the basis of set criteria and some predetermined data points. This method has proven to be more accurate than the interpolation method and would make the work of geographers much easier.
On similar lines, Capegemini developed a Machine Learning framework that uses synthetic aperture radar (SAR) satellite imagery to identify woodland with newly planted trees. It is almost impossible to distinguish between different types of trees in satellite imagery, irrespective of the resolution. Hence for the much-needed clarity and focus, Machine Learning is needed. For making the raw satellite data interpretable for Machine Learning algorithms, it goes through important steps including calibration, smoothing and noise reduction. Once the data modeling is done and predictions are complete, the task is to make it lucid and comprehensible for the analysts.
In the aforementioned cases, we have seen how the scope of using Machine Learning in satellite imagery is not restricted to a narrow field but could be used for modeling, separating images or fetching useful info. The process is complex but the final results are worth the long process. And image analytics could never have been more precise and easy-to-understand.
Expertise in prediction
For demographers and urban planners who rely on the interpretation of satellite imagery, Machine Learning can be used to predict population patterns, urban-rural distribution and poverty levels. This approach will definitely turn over a new leaf in analytics and infuse vibrancy and dynamism in the scientific predictions.
Researchers at Stanford University developed a Machine Learning model that can predict poverty. The model uses satellite imagery to gather data and then executes an algorithm. The algorithm runs through millions of images across the globe comparing the presence of light in any given region during the day and at the night to predict the level of economic activity. This method of comparison is called transfer learning.
The potential of Machine Learning is satellite imagery is immense and would grow rapidly, as more avenues are explored. It will result in levels of unprecedented accuracy. However, at the present, this would require remote sensing professionals to be attuned to the new innovations in Big Data and Machine Learning and be ready for the mega transition that would be unleashed.