Search results for " learning"

Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V

2018

Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…

Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

2020

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…

Understanding deep learning in land use classification based on Sentinel-2 time series

2020

AbstractThe use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims …

Transferring deep learning models for cloud detection between Landsat-8 and Proba-V

2020

Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…

2016

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) gran…

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

2021

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then d…

Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data.

2019

Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with…

Comparing methods for computation of run-up heights of landslide-generated tsunami in the Northern Sicily continental margin

2018

The North Sicily continental margin is a very active region located in the Central Mediterranean. Strong seismicity, active tectonics and volcanism, fluid escape, high sediment supply, and widespread mass movements historically have exposed this region to marine geohazards, with a potential for tsunami generation. Morpho-bathymetric analysis revealed that one of the most common mechanisms associated with marine geohazards is due to submarine mass failure processes, genetically linked to the other processes active in this margin. With the aim to assess the risks associated with landslide-generated anomalous waves, we selected two sectors of this margin, Gulf of Palermo to the west and Patti …

Experimental socioecology: Integrative science for anthropocene landscape dynamics

2016

Abstract The emergence of coupled natural and human landscapes marked a transformative interval in the human past that set our species on the road to the urbanized, industrial world in which we live. This emergence enabled technologies and social institutions responsible for human-natural couplings in domains beyond rural, agricultural settings. The Mediterranean Landscape Dynamics Project (MedLand) is studying the interacting social and biophysical processes associated with these novel socioecological systems and their long-term consequences using a new form of 'experimental socioecology' made possible by recent advances in computation. We briefly describe the MedLand modeling laboratory, …

Potential of science to address the hunger issue: Ecology, biotechnology, cattle breeding and the large pantry of the sea

2018

The knowledge about the real possibilities that current science gives us is basic to support everything that is not negative either for men or for our environment. In this way, it is an advantage to win this battle against hunger with rational use of science advantages. In this paper, we start from the basis that the solution to the problems of hunger requires the multidisciplinary action of sciences and knowledge. We provide a reflection on the possibilities to be considered from disciplines such as ecology, biotechnology, veterinary and aquaculture. The need for ecological studies where the role of human beings as part of ecosystems is considered. In addition, advances in molecular biolog…