DOI: 10.5281/zenodo.4117572
title: Transfer Learning Models and Datasets for a Reliable Emergency Landing Field Identification

description: The file <em>data.tar.gz</em> compromises three HDF5 datasets. This file has been split into 100 files. The files can be merged, decompressed and unpacked with the following commands: <pre><code class="language-bash">cat data* &gt; data.tar.gz tar -xzf data.tar.gz</code></pre> Afterwards, the three files: <em>train_test_data_ss8_supervised_new.hdf5</em>, <em>train_test_data_ss16_supervised_new.hdf5</em>, <em>train_test_data_ss32_supervised_new.hdf5</em> are ready to get processed. Internal structure of the datasets:<br> <strong>Search Window (SW) 8 m^2:</strong><br> HDF5 "train_test_data_ss8_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 76288, 40, 40, 8 ) / ( 76382, 40, 40, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 76382, 1 ) / ( 76382, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 380928, 40, 40, 8 ) / ( 380998, 40, 40, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 380998, 1 ) / ( 380998, 1 ) }<br> }<br> }<br> }} <br> <strong>SW 16 m^2:</strong><br> HDF5 "train_test_data_ss16_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 17024, 80, 80, 8 ) / ( 17054, 80, 80, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 17054, 1 ) / ( 17054, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 84992, 80, 80, 8 ) / ( 85068, 80, 80, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 85068, 1 ) / ( 85068, 1 ) }<br> }<br> }<br> }} <br> <strong>SW 32 m^2:</strong><br> HDF5 "train_test_data_ss32_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 3328, 160, 160, 8 ) / ( 3359, 160, 160, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 3359, 1 ) / ( 3359, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 16768, 160, 160, 8 ) / ( 16793, 160, 160, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 16793, 1 ) / ( 16793, 1 ) }<br> }<br> }<br> }} The sample count of the various generated dataset is as follows: <br> <strong>SW 8 m^2:</strong> {train: 380,928 with {0: 190,464, 1: 190,464}, test: 76,288 with {0: 38,152, 1: 38,136}}<br> <strong>SW 16 m^2: </strong>{train: 84,992 with {0: 42,498, 1: 42,494}, test: 17,024 with {0: 8,516, 1: 8,508}}<br> <strong>SW 32 m^2: </strong>{train: 16,768 with {0: 8,424, 1: 8,344}, test: 3,328 with {0: 1,672, 1: 1,656}} Each sample is composed as follows:<br> RGB = sample[:,:,<strong>:3</strong>]; Theoretically: [0, 1] per color channel<br> NIR = sample[:,:,<strong>3</strong>]; Theoretically: [0, 1]<br> Slope = sample[:,:,<strong>4</strong>]; Theoretically: [0, 90]<br> Roughness = sample[:,:,<strong>5</strong>]; Theoretically: [0, 78.78]<br> NDVI = sample[:,:,<strong>6</strong>]; Theoretically: [-1, 1]<br> DOM = sample[:,:,<strong>7</strong>]; Theoretically: [0, 429.90] ==================================================================================================== The following three files compromise the model and optimizer state variable of our PyTorch models trained on the aforementioned datasets: <em>best_alexnet_final.pth</em>, <em>best_resnet18_final.pth</em>, <em>best_wide_resnet50_2_final.pth</em> Below find a more precise description of each model:<br> <strong>best_resnet18_final.pth</strong> Model: ResNet-18 Dataset: SW 8 Input: RGB-NIR-Slope -&gt; R: [0,224,224], G: [1,224,224], B: [2,224,224], NIR: [3,224,224], Slope: [4,224,224] <strong>best_wide_resnet50_2_final.pth</strong> Model: Wide-ResNet-50-2 Dataset: SW 16 Input: NDVI-Slope -&gt; NDVI: [0,224,224], Slope: [1,224,224] <strong>best_alexnet_final.pth</strong> Model: AlexNet Dataset: SW 32 Input: RGB-Slope -&gt; R: [0,224,224], G: [1,224,224], B: [2,224,224], Slope: [3,224,224] Each model is capable of performing a binary classification, distinguishing between landable and unlandable samples