Remote sensing was originally a technology used solely for military purposes. Gradually, it began to be used for scientific tasks, and today it is a tool employed in an increasing number of sectors worldwide, providing objective information and improving decision-making.
Among the reasons for this widespread adoption are live satellite images, which have helped change the way we understand our planet and manage its resources. To get the most out of this data, data science is the discipline dedicated to the study and transformation of raw data into actionable information ready for use in business processes.
The Evolution of Earth Observation
Similar to other technologies, satellites have evolved from their origins to the present day. There is a large amount of imagery and datasets dating back several decades; this data has great historical value and is very useful in long-term analysis of patterns and trends. However, it cannot compete with current satellite imagery.
Decades ago, the revisit rate of satellites was not sufficient to monitor a specific location or asset comprehensively or in real-time. At the same time, satellite images had modest resolution, in which smaller objects were indistinguishable. The number of spectral bands that satellites could capture was small until the late 1970s, mainly limited to light visible bands and the near-infrared range.
These and other parameters have gradually improved over time, both due to improvements in the instruments on board the satellites themselves and enhancements in the satellites (such as miniaturisation and constellations of satellites working together) and infrastructure (including cloud processing and storage). Thus, current Earth satellite images can provide much more detail than their older counterparts.
Why Data Science Matters?
The massive flow of data and live satellite views presents both a blessing and a problem. The current volume of data far exceeds the capacity of human analysts to extract practical information from it. Furthermore, the processing can vary dramatically depending on the needs. Data science is a multidisciplinary approach combining smart tools, methods, and different technologies to simplify the process of obtaining useful information from raw data.
These are some of the techniques used in data science regarding satellite imagery:
- Preprocessing and noise filtering. Images taken from satellites may contain noise and unwanted elements, such as clouds or shadows. The development of algorithms seeks to correct these imperfections before analysis.
- Feature identification through vegetation indices. Some features of the Earth´s surface are difficult to see with the naked eye, while others are not visible at all. The creation and use of vegetation indices make it easier to recognise these features or quantify values for subsequent analysis.
- Forecasting and trend analysis. Thanks to the recognition of patterns and trends, which require long-term analysis, current models can combine historical data with the most current satellite images to make predictions and determine when specific changes pose a problem.
- Anomaly detection. When is a deviation considered to be within the norm, and when does it require action to be taken? Data scientists train machine learning models to distinguish if a sudden drop is worrisome or a regular behaviour.
Real Life Applications Of Remote Sensing
Thanks to its versatility, satellite data is used in multiple sectors. One of the sectors that makes the most of this data is agriculture. A live satellite view is invaluable for monitoring crop conditions, improving management and input utilization, and detecting whether pests or diseases have affected crops. This enables a timely response to any unforeseen events, thereby protecting the investment throughout the season.
Similarly, forestry is also enhanced by the existence of satellite data. Given its extent, on-site checks are costly and inefficient. Satellite imagery helps detect illegal logging or forest fires, while facilitating long-term monitoring of reforestation activities and the condition of flora and fauna.
Civil engineering is another sector that greatly benefits from satellite technology. The planning of new neighbourhoods or the construction of infrastructure is aided by live satellite views, which can be used to monitor changes in land use or track the construction process, as well as for future studies of urban growth.
During disaster response and risk management, satellite imagery helps create real-time maps, enabling first aid teams to navigate safely through affected areas to deliver aid or evacuate the wounded.
Challenges to be resolved
Despite the current capabilities of satellite data and its potential for improvement, various challenges remain to be resolved. Some of them are the following:
- Data processing infrastructure. The amount of data generated each day is increasing, making it more complicated to process all of it. Cloud systems and scalable architectures are the most promising solutions to address this problem.
- Data verification. Satellite data must be verified using ground truth data, but the system is not as fast as desired.
- Latency and bandwidth. To deliver data in real-time or near real-time, it is essential to have low-latency, high-speed streaming networks.
- Analysis models. Current models are not compatible with some sensors, locations, and other characteristics.
Data science is transforming the Earth observation with more accurate analysis. Thanks to the development of advanced algorithms and machine learning techniques, it is possible to handle large volumes of information to extract patterns and trends effortlessly. It’s not just an improvement in terms of better understanding our planet; it also opens up new opportunities to tackle the global challenges we face.
Author :
Kateryna Sergieieva
Kateryna Sergieieva has a Ph.D. in information technologies and 15 years of experience in remote sensing. She is a scientist responsible for developing technologies for satellite monitoring and surface feature change detection. Kateryna is an author of over 60 scientific publications.
