Transfer Learning Models and Datasets for a Reliable Emergency Landing Field Identification

Name: Transfer Learning Models and Datasets for a Reliable Emergency Landing Field Identification
Creator: Zenodo
Published: 2020-10-23T18:47:52+08:00

Zenodo2020-10-23 更新2026-05-25 收录

紧急着陆场识别

航空安全图像分类

数据链接：

https://zenodo.org/record/4117572 数据链接链接失效反馈

官方服务：

资源简介：

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

提供机构：

Zenodo

创建时间：

2020-10-23

Transfer Learning Models and Datasets for a Reliable Emergency Landing Field Identification

资源简介：

相关数据集