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Found 8 papers, 4 papers with code
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Deep learning models have proven to be effective for this task by mapping time series data to high-level representation for prediction.
Ranked #1 on
Crop Classification
on CropHarvest - Brazil
Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
In this work, we assess the impact of missing temporal and static EO sources in trained models across four datasets with classification and regression tasks.
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task.
Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions.
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance.
Ranked #2 on
Crop Classification
on CropHarvest - Togo
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques.
MLJ: A Julia package for composable machine learning
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages.
Solving the Periodic Timetabling Problem using a Genetic Algorithm
The elaboration of this timetable is done to respond to the commercial requirements for both passenger and freight traffic, but also it must respect a set of security and capacity constraints associated with the railway network, rolling stock and legislation.
