Novel approach for forest road maintenance using smartphone sensor data and deep learning methods

High costs primarily pose challenges to forest management in planning and executing the repair of forest roads. With budget limitations and inadequate oversight, it has become critically essential to monitor the state of these roads. Monitoring the condition of forest roads has become imperative, driven by budget constraints and a lack of effective supervision. While smartphones have proven effective in detecting road defects on public roads, their application on forest roads is hindered by the absence of suitable indices and software infrastructure. Addressing this gap, this research focuses on the development of the Forest Road Pavement Condition Index (FRPCI) to facilitate smartphone-based monitoring. We collected and compared data from 4 kilometers of forest roads, employing two traditional harvesting methods alongside smartphone sensor data. Utilizing deep learning methods, including Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and CNN-LSTM, we processed the collected data. Signal processing using GPS data, coupled with wavelet transformation, demonstrated promising results with an accuracy and recall exceeding 80%. The proposed system functions as a distributed information system, transitioning data from organizational mode to field mode. It measures damage, assesses forest road conditions, and leverages image processing and GPS technologies. This monitoring system technology offers capabilities for preparing, storing, updating, maintaining, and analyzing diverse information. Importantly, adopting this method can significantly reduce operating costs, making forest road monitoring for maintenance purposes more feasible.