> ## Documentation Index
> Fetch the complete documentation index at: https://lss-lab.kankyo.tohoku.ac.jp/llms.txt
> Use this file to discover all available pages before exploring further.

# Data & methods

> The Earth-observation data, geospatial tools, statistical methods, and models used in the lab.

## Earth observation

We work primarily with open satellite archives, chosen to match the scale and time span of the
question:

| Source                                       | Typical use                                                |
| -------------------------------------------- | ---------------------------------------------------------- |
| Landsat (5/7/8/9)                            | Long-term land-cover change, land-surface temperature      |
| Sentinel-1 / Sentinel-2                      | Recent high-resolution mapping, cloud-resilient monitoring |
| MODIS / VIIRS                                | Daily-to-annual regional and global time series            |
| Global land-cover and forest-change products | Baselines and cross-validation                             |
| Satellite atmospheric products               | Air-quality and pollution exposure                         |

## Geospatial data science

Analyses are built on cloud and desktop geospatial stacks: **Google Earth Engine** for large-area
time-series processing, **GDAL** and Python for local processing pipelines, and **ArcGIS Pro**,
**ENVI**, and **QGIS** for mapping, image analysis, and cartography.

## Statistics & modelling

* **Spatial statistics** — autocorrelation, spatial regression, hot-spot and change detection.
* **Driver attribution** — linking observed land-system change to socio-economic and biophysical factors.
* **Land-system modelling** — scenario simulation, including agent-based and cellular approaches to
  land-use change.
* **Deep learning** — convolutional and transformer models for classification, segmentation, and
  prediction on Earth-observation data, implemented in **PyTorch**.

## Reproducibility

We aim to keep analyses scripted end-to-end, version-controlled, and documented so that results can
be reproduced and extended. Code and derived datasets are released where licensing and agreements
allow — see [Data & code](/en/resources/datasets).