What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Mathematical morphology, Artificial intelligence, Algorithm, Segmentation and Image processing are his primary areas of study.
His studies in Mathematical morphology integrate themes in fields like Cartography, Pixel, Image formation and Iterative reconstruction.
He has included themes like Discrete geometry and Computer vision in his Artificial intelligence study.
His work in the fields of Computer vision, such as Digital image, intersects with other areas such as Random model.
He interconnects Structuring element, Geometry, Discrete mathematics and Grayscale in the investigation of issues within Algorithm.
His study in the field of Image segmentation also crosses realms of Refugee.
His most cited work include:
- Watersheds in digital spaces: an efficient algorithm based on immersion simulations (4587 citations)
- Morphological Image Analysis: Principles and Applications (2711 citations)
- Morphological Image Analysis (1171 citations)
What are the main themes of his work throughout his whole career to date?
Pierre Soille focuses on Artificial intelligence, Mathematical morphology, Computer vision, Image processing and Pixel.
His study in Segmentation, Image segmentation, Image and Region growing falls under the purview of Artificial intelligence.
His Image segmentation study combines topics from a wide range of disciplines, such as Watershed and Feature extraction.
His work carried out in the field of Mathematical morphology brings together such families of science as Spatial analysis, Geometry, Line segment, Geodesic and Algorithm.
As a part of the same scientific family, Pierre Soille mostly works in the field of Image processing, focusing on Remote sensing and, on occasion, Cartography and Land cover.
Specifically, his work in Pixel is concerned with the study of Grayscale.
He most often published in these fields:
- Artificial intelligence (44.08%)
- Mathematical morphology (36.84%)
- Computer vision (32.89%)
What were the highlights of his more recent work (between 2013-2020)?
- Earth observation (6.58%)
- Artificial intelligence (44.08%)
- Big data (3.95%)
In recent papers he was focusing on the following fields of study:
Pierre Soille spends much of his time researching Earth observation, Artificial intelligence, Big data, Contextual image classification and Convolutional neural network.
His study looks at the relationship between Earth observation and fields such as Remote sensing, as well as how they intersect with chemical problems.
His research brings together the fields of Computer vision and Artificial intelligence.
His Computer vision research incorporates elements of Discretization and Radiometry.
His study looks at the relationship between Contextual image classification and topics such as Data mining, which overlap with Image resolution, Pixel, Urban area and Classifier.
The various areas that Pierre Soille examines in his Segmentation study include Algorithm, Mathematical morphology and Mutual information.
Between 2013 and 2020, his most popular works were:
- Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas (101 citations)
- Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping (51 citations)
- A versatile data-intensive computing platform for information retrieval from big geospatial data (44 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
His scientific interests lie mostly in Human settlement, Remote sensing, Earth observation, Thematic map and Grid.
His Human settlement studies intersect with other subjects such as Data analysis, Data science, Big data, Service and Volume.
His research integrates issues of Image resolution, Information extraction, Feature extraction and Resolution in his study of Remote sensing.
His Earth observation study integrates concerns from other disciplines, such as Cartography, Built-up area and Satellite data.
His Thematic map research is multidisciplinary, relying on both Classifier, Contextual image classification, Urban planning and Urban area.
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