地球资源数据云——数据资源详情
溢油分类
该数据集《Oil Spill Classification》主要用于二分类任务，数据形态以图像为主，应用场景偏向天文科学。 题目说明：Predicting whether an Ocean patch contains an oil spill or not. 任务类型：图像二分类。 建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：oil_spill.csv。 The dataset was developed by starting with satellite images of the ocean, some of which contain an oil spill and some that do not. Images were split into sections and processed using computer vision algorithms to provide a vector of features to describe the contents of the image section or patch. The task is, given a vector that describes the contents of a patch of a satellite image, then predicts whether the patch contains an oil spill or not, e.g. from the illegal or accidental dumping of oil in the ocean. There are two classes and the goal is to distinguish between spill and non - spill using the features for a given ocean patch. Non - Spill: negative case, or majority class. Oil Spill: positive case, or minority class.
摘要概览
该数据集《Oil Spill Classification》主要用于二分类任务，数据形态以图像为主，应用场景偏向天文科学。 题目说明：Predicting whether an Ocean patch contains an oil spill or not.
任务类型：图像二分类。
建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：oil_spill.csv。
The dataset was developed by starting with satellite images of the ocean, some of which contain an oil spill and some that do not. Images were split into sections and processed using computer vision algorithms to provide a vector of features to describe the contents of the image section or patch.
The task is, given a vector that describes the contents of a patch of a satellite image, then predicts whether the patch contains an oil spill or not, e.g. from the illegal or accidental dumping of oil in the ocean.
There are two classes and the goal is to distinguish between spill and non - spill using the features for a given ocean patch.
Non - Spill: negative case, or majority class. Oil Spill: positive case, or minority class.