Machine Learning is getting popular in Remote Sensing
Machine Learning
Machine Learning (ML) is a branch of Artificial
Intelligence (AI). Algorithms can be modified and learn from an experience and
material without being programmed especially, minimizing the human intervention
level. The model's output quality relies upon the data quality utilized for
building it. Models of ML can be classified into supervised or unsupervised or
a mixture. Models of supervised learning are assembled on data of labeled
training. For instance, utilizing the pictures of trees known for identifying
the trees in a new image. Unsupervised learning models are assembled on unlabeled
training data, such as exposing millions of unlabeled tree pictures for
extracting the similar features that form up the tree and recognizing it in an
earlier unseen picture.
Forecasting of
Weather
Physical models are the designed operational tools for
weather forecasting. Though, ML is utilized along with designed methods for
advanced analysis and addressing challenges. For instance, the Meteorology
Office utilizes ML for removing the interference in calculations of rainfall.
These calculations are served in tools forecasting and used by the flood
forecasting Environment Agency.
Precision of
Agriculture
The new technology's uptake for boosting farming
efficiency, referred to as the precision of agriculture, is less in the United
Kingdom. For improving the AI uptake, several million grants are made available
by the government for the Transforming Production of Food. ML can be applied
for the management of crops, water, livestock, and soil. For instance, the health
of crops can be estimated from the images of remote sensing, when the
chlorophyll abundance and the green pigments are responsible for the process of
photosynthesis, which performs as a health indicator.
Drought and Flood
management and prediction
Management of water resources, forecasting rainfalls,
climate modeling advancements all provender drought and flooding management and
their predictions. Drona capabilities and satellites are collaborated with ML
for emergency response, recovery efforts, detection, and mitigation.
Forests Management
ML is used for managing forests (logistics,
harvesting, cropping) and in disease monitoring in species of forests. It can
further be utilized in deriving cover change of forest and deforestation from
remotely sensed images. For instance, ML is being used for tracking and illegal
logging of police in real-time of tropical rainforests by sending information
to local services through an application of phone.
Research of Marine
Drone data and Satellite by ML can be utilized to map
marine habitat and monitor coastal alteration because of landslides and in
projects of coastal plastic cleaning. For instance, researchers have
established an algorithm for permitting collected patches detection of plastic
floating from satellite images of coastal waters.
Benefits of Machine Learning in Remote Sensing
In collaborating with remote sensing, ML can provide help for improving the expert's comprehension of the atmosphere, land, and ocean system. This can lead towards many benefits and these are the following:
Improving environment
system behaviors
ML has been utilized for reducing the doubts related
to aerosols' role in the climate system by joining the models of aerosols in a
map of the atmosphere. ML is further utilized for combining the projection
models of climate in a manner that well accounts for the weaknesses and
strengths of individual models that might take towards many realistic
projections.
Improved data
analysis automation
ML also helps in the automation of data analysis.
Remotely sensed pictures are among the most significant sources of environmental
sciences. ML algorithms have updated the image classification by using the more
accessible data, saving time, and minimizing errors introduced by human
analysts.
Better resource
management
Data for vital natural resource management like soil
and water can be produced utilizing ML. The hydrological cycle's components, such
as available surface water amount, rainfall, and evaporating rates, are
estimated with remote sensing technologies. ML is utilized for converting the
worldwide satellite data of moisture soil, as a soil health indicator, into
more correct estimations at local and regional levels.
New insights discovery
from complex datasets
ML has helped in making the processing of large
datasets much faster and feasible. With algorithms, large datasets can be
processed for extracting data and patterns derive, which may be too time-consuming
or complex for human analysts. For instance, the digital twins' development
(physical system's virtual interactive model) and Belmap, a Belgium 3D model,
is designed with ML for enhancing the remotely sensed data.
Unwanted data
management
Clouds are usually a feature of satellite pictures
blocking sightlines towards the ground or create shadows that cause prevention
in the analysis. ML algorithms help distinguish between the helpful Earth
pictures and clouds by the enhanced automation and correction with a technique
known as "cloud masking." The advancement in this area helps to
reduce the delay between when the data collected and when it can be ready for
use.
Conclusion
The roots of ML in remote sensing since the 1990s, but
for the last ten years, remote sensing is being very popular. Initially, it was
started with a way to knowledge-based building automation for remote sensing. Now
it is being utilized in all types of projects, such as from unsupervised
satellite images classification of scenes to the Australian native
classification of forests. Additionally,
ML is critical as the above-mentioned applications and benefits have shown that
ML has increased almost in every field. Many researchers have been using it,
and even NASA is providing training
about remote sensing. This learning series conducted by NASA is the Applied
Remotely Sensing Training Program (ARSET),
and it is part of NASA's Capacity Building Program of Applied Sciences. ESA is also providing training courses
in remote sensing, as its "10th
ESA Advanced Training Course on Land Remote Sensing 2021" will
start from 20 September and end on 24th September.
10th ESA Advanced Training Course on
Land Remote Sensing 2021.
(2021.). Eo Science for Society. Retrieved February 14, 2021, from https://eo4society.esa.int/event/10th-advanced-land-2021/
Maxwell,
A.E., Warner, T.A., Fang, F., (2018). Implementation of machine-learning
classification in remote sensing: an applied review. Int. J. Remote Sens. 39,
2784–2817. https://doi.org/10/gfghjc
NASA Learning: Fundamentals of Remote Sensing
| World Bank Group.
(2015). Olc.Worldbank.Org. Retrieved February 14, 2021, from https://olc.worldbank.org/content/nasa-learning-fundamentals-remote-sensing
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