VistaFormer: Simple Vision Transformers for Satellite Image Time Series Segmentation

The dataset includes trained models, training logs, and test results on PASTIS and MTLCC semantic segmentation benchmark datasets. Both benchmark datasets these models are trained on are crop-type classification benchmarks that use time series Sentinel data as inputs. The PASTIS benchmark covers agricultural land plots in France while the MTLCC benchmark covers agricultural land plots in Germany. Code that accompanies these trained weights and records as well as code that can be used to transform benchmark inputs for use by the trained models can be found here: https://github.com/macdonaldezra/VistaFormer本数据集包含在PASTIS与MTLCC语义分割基准数据集上训练得到的模型、训练日志与测试结果。这些模型所训练使用的两个基准数据集，均属于以时序Sentinel（哨兵）卫星数据为输入的作物类型分类基准任务。其中PASTIS基准数据集覆盖法国境内的农业用地地块，MTLCC基准数据集则涵盖德国境内的农业用地地块。随附代码包含配套训练权重与记录的处理代码，以及可用于转换基准输入以适配已训练模型的代码，相关代码可在此处获取：https://github.com/macdonaldezra/VistaFormer

搜集汇总

背景与挑战

背景概述

该数据集包含用于卫星图像时间序列语义分割的VistaFormer预训练模型、训练日志和测试结果，基于PASTIS（法国）和MTLCC（德国）两个农业作物分类基准数据集，使用Sentinel时间序列数据作为输入，旨在支持遥感环境监测和计算机视觉研究。