태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
The objective of this challenge is to automate the process of estimating the soil parameters, specifically, potassium (KKK), phosphorus pentoxide (P2O5P_2O_5P2O5), magnesium (MgMgMg) and pHpHpH, through extracting them from the airborne hyperspectral images captured over agricultural areas in Poland (the exact locations are not revealed). To make the solution applicable in real-life use cases, all the parameters should be estimated as precisely as possible.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
KITTI360-EX is a dataset for outer- and inner FoV expansion. It contains 76k pinhole images as well as 76k spherical images and is used for beyond-FoV estimation. Source: FlowLens: Seeing Beyond the FoV via Flow-guided Clip-Recurrent Transformer Image Source: https://arxiv.org/pdf/2211.11293v1.pdf
The dataset comprises 2886 patches in total (2 m GSD), of which 1732 patches for training and 1154 patches for testing. The patch size varies (depending on agricultural parcels) and is on average around 60x60 pixels. Each patch contains 150 contiguous hyperspectral bands (462-942 nm, with a spectral resolution of 3.2 nm), which reflects the spectral range of the hyperspectral imaging sensor deployed on-board Intuition-1. The participants are given a training set of 1732 training examples. The examples are hyperspectral image patches with the corresponding ground-truth information. Each masked patch corresponds to a field of interest. Ground truth are the soil parameters obtained for the soil samples collected for each field of interest in the process of laboratory analysis, and is represented by a 4-value vector.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.