five

CE_DeepCraters|月球撞击坑数据集|深空探测数据集

收藏
DataCite Commons2020-08-25 更新2024-08-17 收录
月球撞击坑
深空探测
下载链接:
https://figshare.com/articles/CE_DeepCraters/12768539
下载链接
链接失效反馈
资源简介:
# DeepCraters Lunar Craters Database 2020-07<br>This is a database of 117240 lunar impact craters larger than about 1 km in diameter. Craters were identified on 120m CE-1, 50m CE-2 DOM and DEM images captured by Chang’E-1 (CE-1) and Chang’E-2 (CE-2) orbiters. And that the formation Systems of new detected craters, 18,996 larger than 8km, are estimated.<br><br>## Method review<br>The database of 117240 lunar impact craters are identified by the trained R-FCN model and the age of craters is estimated by a dual-channel classification model with the semisupervised method. (the details of method can be found in our paper[1]).<br><br><br>## Briefly Description<br>The database contains two comma-delimited table files: `Lunar_Crater_Database_DeepCraters_2020.csv` and `Aged_Lunar_Crater_Database_DeepCraters_2020.csv`. <br><br>&gt; The first table is the detected craters larger than about 1 km in diameter, which contains 117240 craters with 5 common fields in each crater. What needs illustration is that the first 7009 craters contains additional 2 fields `Lat_new` and `Lon_new` because of the inaccuracy of the CE1 dataset. the details of each field are listed below:<br>&gt; - `Flags_data`: 'CE1' or 'CE2' indicats the crater detected from CE-1 or CE-2 data;<br>&gt; - `ID`: the index of craters;<br>&gt; - `Lat`: the latitude of crater center in degree;<br>&gt; - `Lon`: the longitude of crater center in degree;<br>&gt; - `Diam_km`: the diameter of crater in kilometer;<br>&gt; - `Lat_new`: the latitude of the corresponding crater in Robbins Lunar Crater Database[2] in degree;<br>&gt; - `Lon_new`: the longitude of the corresponding crater in Robbins Lunar Crater Database[2] in degree.<br>&gt;<br><br>&gt; The second table is the aged craters larger than 8 km in diameter, which contains 18996 craters with 6 fields in each craetr. And the first 5 fields are the same as in the first table, the last field `Age` indicates the system that the crater belongs to.<br>&gt;<br>&gt; - `Age`=1: pre-Nectarian System<br>&gt; - `Age`=2: Nectarian System<br>&gt; - `Age`=3: Imbrian System<br>&gt; - `Age`=4: Eratosthenian System <br>&gt; - `Age`=5: Copernican System<br><br><br><br>--------<br><br>## Note<br><br>The diameter of craters detected by DeepCraters is slightly larger than the real size of the craters. <br><br><br><br><br>## Reference<br>[1] Yang C , Zhao H , Bruzzone L , et al. Lunar impact craters identification and age estimation with Chang'E data by deep and transfer learning. [Online]. https://nature-research-under-consideration.nature.com/posts/57165-lunar-impact-craters-identification-and-age-estimation-with-chang-e-data-by-deep-and-transfer-learning.<br><br>[2] Robbins, S. J. A new global database of lunar impact craters &gt;1–2 km: 1. crater locations and sizes, comparisons with published databases, and global analysis. Journal of Geophysical Research (Planets),124(4), 871-892(2019).<br>
提供机构:
figshare
创建时间:
2020-08-06
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4099个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

resume-conversations-llm-training

这是一个高质量的职业对话数据集,适用于构建能够理解简历、职业和职业成长的AI。数据集以结构化的JSONL格式提供,包含关于职业发展、技术趋势和专业技能的现实问答,非常适合开发者和AI实践者用于聊天机器人、职业咨询工具或LLM微调。

huggingface 收录

MIDV-500

该数据集包含使用移动设备拍摄的不同文档图像,这些图像通常具有投影变形。数据集分为训练和测试两部分,其中训练部分包含30种文档类型,测试部分包含20种,在应用神经网络之前,所有图像都被缩放到统一的宽度,宽度为400像素。该数据集的任务是进行消失点检测。

arXiv 收录

日食计算器

此日食计算器能够查询公元前3000至后3000年范围内的日食信息,生成每次日食的覆盖区、中心区范围数据,展示日食带的地图;并可根据用户在地图上点击的坐标在线计算该地日食各阶段时间、食分等观测信息。

国家天文科学数据中心 收录

PCLT20K

PCLT20K数据集是由湖南大学等机构创建的一个大规模PET-CT肺癌肿瘤分割数据集,包含来自605名患者的21,930对PET-CT图像,所有图像都带有高质量的像素级肿瘤区域标注。该数据集旨在促进医学图像分割研究,特别是在PET-CT图像中肺癌肿瘤的分割任务。

arXiv 收录

Allen Brain Atlas

Allen Brain Atlas 是一个综合性的脑图谱数据库,提供了详细的大脑解剖结构、基因表达数据、神经元连接信息等。该数据集包括了小鼠、人类和其他模式生物的大脑数据,旨在帮助研究人员理解大脑的结构和功能。

portal.brain-map.org 收录