How self-supervised learning and quantum computing will transform remote sensing
Vikas Mishra
Quantum theory is rapidly evolving in various advanced technologies with impact to space applications. We are at the beginning of a paradigm shift, which is largely impacting Earth Observation (EO). An increasing amount of research in communications, optics, physics, nanotechnologies, large scale Artificial Intelligence (AI) innovations like quantum computing, generative AI and self-supervised learning (SSL) techniques etc provide fantastic opportunities to implement disruptive data analytics methods or instruments for space applications.
Exercise Vayu Shakti-24Quantum technology is already present in space, e.g., quantum key distribution. Latest investigation results validate the feasibility of actual applications and many other resources with unprecedented potentials such as quantum simulations, computing, imaging, sensing, metrology, optimisation or machine learning are at the edge of maturity. The open or easy access to early quantum computers represents a huge potential to discover new solutions and broaden the applications of EO.
Quantum computing can solve challenges of the Big EO Data and AI solutions. BigGIS refers to the integration of large-scale geographic information systems with big data technologies to manage and analyse vast amounts of geospatial data. This includes data from numerous Earth observation satellites and close range collection techniques such as LiDAR and mobile devices. As data collection outpaces consumption, integrating AI techniques for BigGIS becomes critical to effectively tackle the challenges it presents. Innovations such as SSL, quantum computing (QC) and generative AI enhance data processing and interpretation while providing new strategies for domain adaptation and transfer learning.
BigGIS
Remote sensing has evolved to become the primary technology supporting environmental monitoring, urban planning, agriculture, and defence in the age of big data. The retrieval of data, efficiently done through satellites, drones and other remote platforms, provides insights into a wealth of information about the surface and atmosphere of the earth. It can quickly provide static or dynamic geospatial data with varied scales and resolutions. The amount of data generated by aerial platforms can run into massive quantities, but very often, in dealing with geospatial data, storage and real-time processing remain challenging. It is the need of the hour to extract valuable information from these remote sensing data using computationally efficient techniques. This is where quantum computing, with its unparalleled computational power, offers promising solutions. BigGIS refers to the integration of largescale GIS with big evolving niche data technologies like generative AI, quantum computing etc to manage and analyse vast amounts of geospatial data generated by aerial or mobile platforms.
Although machine learning applications have demonstrated better accuracy than those using traditional algorithms or classifiers. There are several issues that hinder breakthroughs in remote sensing data classification viz complexity of algorithms and huge volume of remote sensing geospatial data and associated difficulties in distinguishing intra-class diversity and inter-class similarity, variations in scene images at different scales and the challenge of processing scenes with multiple objects, among others. With the evolution of machine learning techniques such as deep learning or CNN that have been employed to resolve these data intensive problems, such trend makes it imperative to have more powerful computing resources and extensive training datasets. The escalating demand for exceptional computing power and the shortage of largescale datasets due to the labour-intensive process of creating them, have become a bottleneck for remote sensing data processing and analysis in near real time for critical missions viz disaster monitoring, surveillance etc.
Understanding BigEO Data
Remote sensing depends on satellite, aircraft, mobile or aerial platforms, drones etc equipped with sensors to capture data across bands of spectral or spectrum. The main challenges associated with EO or remote sensing are as enumerated below:
Data Volume: The data volume created from remote sensing platforms is enormous. Most of the time, this data is multidimensional, involving spatial, spectral, temporal dimensions and these are complex to process and analyse.
Real time Processing: Real time data analysis of geospatial data is significant for time critical application or use, but because of the performance bottleneck of the classical computing systems, delays start creeping in, which eventually affect real time decision-making.
Data Fusion: Data fusion is the process of integrating multiple data sources viz different sensors and platforms to produce more consistent, accurate and useful information than that provided by any individual data source. Besides, the great heterogeneity of data sources also adds to the difficulty of the fusion process and demands high computational power.
Self-Supervised Learning in Remote Sensing
Self-Supervised Learning (SSL) is an innovative learning method that is gaining increasing importance in the world of machine learning. While SSL’s use in quantum machine learning (QML) is still largely experimental, it has the potential to enhance QML’s capabilities. QML combines the computational power of quantum computers with modern learning methods to analyse large datasets more efficiently and accurately. It provides a way to utilise this geospatial data in a manner that traditional methods often cannot achieve the desired result in given time-frame in view of huge geospatial data being generated by remote sensing.
In self-supervised learning, a model or algorithm is trained to learn autonomously from geospatial or EO data in near real time without relying on fully labelled datasets. While in supervised learning methods, each data point is assigned a unique label and in unsupervised methods, no labels are present at all, SSL lies between these two extremes. In SSL, the data is partially used to generate artificial labels or tasks that the model must solve during training. An example of this is predicting and completing missing parts in a dataset, whether it’s a missing word in a sentence or a missing section of an image. This approach allows the model to recognise deeper relationships and patterns in the geospatial data and learn from them, ultimately leading to better results when applied to fully labelled data.
Quantum Computing and Its Principles
Traditional computers operate on concept of bits, which can be either 0 or 1. Quantum computers leverage the principles of quantum mechanics, where qubits exist in a superposition state i.e., they can be 0, 1 or both simultaneously. This unique ability, along with two other quantum phenomena viz entanglement and interference, empowers quantum computers to solve specific problems exponentially faster than classical computers specially in space domain or remote sensing. By harnessing these principles, quantum computers can tackle problems that would take classical computers years, if not centuries, to solve. The details of these fundamental principles are as elaborated below:
(a) Superposition: Think of flipping a coin that does not just land on heads or tails but can be both simultaneously. This is the essence of superposition in quantum computing. Qubits can exist in a combination of 0 and 1 states until measured, allowing them to explore multiple state or possibilities simultaneously.
(b) Entanglement: This phenomenon creates a special connection between qubits, even when physically separated. It’s like having two coins that always land on the same side, no matter how far apart. Measuring one entangled qubit instantly determines the state of the other, regardless of distance. This concept of interconnectedness enables parallel processing across multiple qubits, accelerating computations.
(c) Quantum Interference: When multiple state or possibilities for a qubit’s state overlap, they can either amplify or cancel each other out. This phenomenon allows quantum algorithms to exploit constructive interference to find solutions efficiently.
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