# Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection

arXiv: https://arxiv.org/abs/2411.08171 · HTML: https://mlsanity.com/paper/2411.08171/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [Transferability in Deep Learning: A Survey](https://mlsanity.com/paper/2201.05867/) (2022)
- [Transfer Learning based Dynamic Multiobjective Optimization Algorithms](https://mlsanity.com/paper/1612.06093/) (2016)
- [DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications](https://mlsanity.com/paper/2305.09018/) (2023)
- [Exponentially Stable Observer-based Controller for VTOL-UAVs without Velocity Measurements](https://mlsanity.com/paper/2205.07159/) (2022)
- [Observer-based Controller for VTOL-UAVs Tracking using Direct Vision-Aided Inertial Navigation Measurements](https://mlsanity.com/paper/2212.14632/) (2022)
- [UWB Ranging and IMU Data Fusion: Overview and Nonlinear Stochastic Filter for Inertial Navigation](https://mlsanity.com/paper/2308.13393/) (2023)
- [SegNet: A Segmented Deep Learning based Convolutional Neural Network Approach for Drones Wildfire Detection](https://mlsanity.com/paper/2405.00031/) (2024)
- [Deep Reinforcement Learning for Sim-to-Real Policy Transfer of VTOL-UAVs Offshore Docking Operations](https://mlsanity.com/paper/2406.00887/) (2024)
- [Geometric Stochastic Filter with Guaranteed Performance for Autonomous Navigation based on IMU and Feature Sensor Fusion](https://mlsanity.com/paper/2108.11866/) (2021)
- [A Geometric Nonlinear Stochastic Filter for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2102.06150/) (2021)
- [Quaternion-based Adaptive Backstepping Fast Terminal Sliding Mode Control for Quadrotor UAVs with Finite Time Convergence](https://mlsanity.com/paper/2407.01275/) (2024)

## Built on by (most cited first, 2 of 2)

- [Novel Multi-Agent Action Masked Deep Reinforcement Learning for General Industrial Assembly Lines Balancing Problems](https://mlsanity.com/paper/2507.16635/) (2025)
- [TileNet: Tile-Based CNN-SVM Architecture for Autonomous Unmanned Aerial Systems Inspection of Flat Roofs](https://mlsanity.com/paper/2609.13013/) (2026)

## Abstract (arXiv)

Contemporary Artificial Intelligence (AI) and Machine Learning (ML) research places a significant emphasis on transfer learning, showcasing its transformative potential in enhancing model performance across diverse domains. This paper examines the efficiency and effectiveness of transfer learning in the context of wildfire detection. Three purpose-built models -- Visual Geometry Group (VGG)-7, VGG-10, and Convolutional Neural Network (CNN)-Support Vector Machine(SVM) CNN-SVM -- are rigorously compared with three pretrained models -- VGG-16, VGG-19, and Residual Neural Network (ResNet) ResNet101. We trained and evaluated these models using a dataset that captures the complexities of wildfires, incorporating variables such as varying lighting conditions, time of day, and diverse terrains. The objective is to discern how transfer learning performs against models trained from scratch in addressing the intricacies of the wildfire detection problem. By assessing the performance metrics, including accuracy, precision, recall, and F1 score, a comprehensive understanding of the advantages and disadvantages of transfer learning in this specific domain is obtained. This study contributes valuable insights to the ongoing discourse, guiding future directions in AI and ML research. Keywords: Wildfire prediction, deep learning, machine learning fire, detection