add part of readme back

README.md

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+# Seg2Map :mag_right: :milky_way:
+
+_An interactive web map app for applying Doodleverse/Zoo models to geospatial imagery_
+
+![](https://user-images.githubusercontent.com/3596509/194389595-82ade668-daf0-4d24-b1a0-6ecf897f40fe.gif)
+
+![separate_seg_controls_demo (1)](https://github.com/Doodleverse/seg2map/assets/61564689/d527fe8c-c3f2-4c62-b448-e581162e8475)
+
+## Overview:
+
+- Seg2Map facilitates application of Deep Learning-based image segmentation models and apply them to high-resolution (~1m or less spatial footprint) geospatial imagery, in order to make high-resolution label maps. Please see our [wiki](https://github.com/Doodleverse/seg2map/wiki) for more information.
+
+- The principle aim is to generate time-series of label maps from a time-series of imagery, in order to detect and assess land use/cover change. This project also demonstrates how to apply generic models for land-use/cover on publicly available high-resolution imagery at arbitrary locations.
+
+- Imagery comes from Google Earth Engine via [s2m_engine](https://github.com/Doodleverse/s2m_engine). Initially, we focus on [NAIP](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-aerial-photography-national-agriculture-imagery-program-naip) time-series, available for the conterminious United States since 2003. In the future, [Planetscope](https://developers.planet.com/docs/data/planetscope/) imagery may also be made available (for those with access, such as federal researchers).
+
+- We offer a set of [Segmentation Zoo](https://github.com/Doodleverse/segmentation_zoo) models, especially created and curated for this project based on a publicly available datasets. These datasets have been selected because they are public, large (several hundred to several thousand labeled images), and provide broad class labels for generic land use/cover mapping needs.
+
+# Installation Instructions
+
+In order to use seg2map you need to install Python packages in an environment. We recommend you use [Anaconda](https://www.anaconda.com/products/distribution) to install the python packages in an environment for seg2map. After you install Anaconda on your PC, open the Anaconda prompt or Terminal in Mac and Linux and use the `cd` command (change directory) to go the folder where you have downloaded the seg2map repository.
+
+1. Create an Anaconda environment
+
+- This command creates an anaconda environment named `seg2map` and installs `python 3.10` in it
+- You can also use `python 3.10`
+- We will install the seg2map package and its dependencies in this environment.
+  ```bash
+  conda create --name seg2map python=3.10 -y