Earth observation brain (EOB): an intelligent earth observation system [PDF]
Geo-spatial Information Science, 2017 Since the twenty-first century, with the rapid development of high-resolution earth observation satellites, the earth observation satellite system has developed from the initial single satellite observation model to the current satellite constellation ...
Deren Li +4 more
doaj +4 more sources
An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
Remote Sensing, 2022 As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment.
Yongguang Zhang +2 more
exaly +2 more sources
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction. [PDF]
Sci RepMachine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative ...
Singh G +5 more
europepmc +3 more sources
AUTOMATED CO-REGISTRATION OF INTRA-EPOCH AND INTER-EPOCH SERIES OF MULTISPECTRAL UAV IMAGES FOR CROP MONITORING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 The application of UAV-based aerial imagery has advanced exponentially in the past two decades. This can be attributed to UAV operational flexibility, ultra-high spatial resolution, inexpensiveness, and UAV-based sensors enhancement.
P. O. Mc’Okeyo +3 more
doaj +1 more source
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery [PDF]
Computer Vision and Pattern Recognition, 2023 Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation. Nevertheless, these works primar-ily focus on a single modality without temporal and geo-context modeling, hampering their ...
Xin Guo +15 more
semanticscholar +1 more source
LOW-LEVEL TIE FEATURE EXTRACTION OF MOBILE MAPPING DATA (MLS/IMAGES) AND AERIAL IMAGERY [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Mobile Mapping (MM) is a technique to obtain geo-information using sensors mounted on a mobile platform or vehicle. The mobile platform’s position is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation ...
P. Jende +5 more
doaj +1 more source
DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when ...
L. Madhuanand, F. Nex, M. Y. Yang
doaj +1 more source
Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward [PDF]
IEEE Geoscience and Remote Sensing Magazine, 2023 Earth observation is increasingly used for mapping and monitoring processes occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a global view, consistent in time, of the state of our forests, oceans, and growing urban
D. Tuia +13 more
semanticscholar +1 more source
Retrieving microphysical properties of dust-like particles using ellipsoids: the case of refractive index [PDF]
Atmospheric Chemistry and Physics, 2015 Distributions of ellipsoids are often used to simulate the optical properties of non-ellipsoidal atmospheric particles, such as dust. In this work, the applicability of ellipsoids for retrieving the refractive index of dust-like target model particles ...
O. Kemppinen +3 more
doaj +1 more source
There Are No Data Like More Data: Datasets for deep learning in Earth observation [PDF]
IEEE Geoscience and Remote Sensing Magazine, 2023 Carefully curated and annotated datasets are the foundation of machine learning (ML), with particularly data-hungry deep neural networks forming the core of what is often called artificial intelligence (AI).
Michael Schmitt +6 more
semanticscholar +1 more source