@@ -7,10 +7,10 @@ Official TensorFlow Code base for "Automated delineation of the agricultural fie
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## Introduction
*Coming soon*
Digital agricultural services (DAS) rely on timely and accurate spatial information of agricultural fields. Initiatives, including deep learning (DL), have been used to extract accurate spatial information using remote sensing images. However, DL approaches require a large amount of fully segmented and labelled field boundary data for training that is not readily available. Obtaining high-quality training data is often costly and time-consuming. To address this challenge, we develop a multi-scale, multi-task DL-based novel architecture with two modules, an edge enhancement block (EEB) and a spatial attention block (SAB), using partial training data (i.e., weak supervision). This architecture is capable of delineating narrow and weak boundaries of agricultural fields
## Using the code
The code is stable while using Python 3.10.14, CUDA >= 11.0 and TensorFlow 2.17.0.
The code is stable while using *Python 3.10.14*, *CUDA >= 11.0* and *TensorFlow 2.17.0*.
The Python dependencies can be installed using conda or pip:
```
...
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@@ -96,7 +96,6 @@ inputs
├── 003.tif
├── ...
```
*Coming soon*
## Pretrained weight
*A pretrained weight will be released soon after the paper is accepted.*
