Ai python.

Inspired by the works referenced as MLMR, we seek to replicate the random forest classifier workflow with different classes. Our requirements for this workflow are as follows: • The input dataset will be .RCP / .E57 • A reformatting workflow will be proposed to transform the data into .TXT/.LAS or similar • Point features should include X,Y,Z,R,G,B, normals & class as minimum • The Random Forest classifier will have 3/4 layers/levels that will drill into greater details of the dataset and more complex classes • The training dataset will be only 10-15% of the full dataset • The training dataset will be manually labelled • The RF classifier will use the manually labelled dataset and perform the classification for the remaining 85-90% of the dataset. • A visualisation of the classified dataset will appear • Each class will be saved separately • Each class will save each instance separately. We have found that point cloud files are huge in size and require a significant time to process hence why the freelancer needs to take this into consideration. A small sample “room” file has been provided with this brief, but this project should allow for input files of up to 500GB. A 200GB dataset sample can be provided upon request. In general, we will like the freelance to design the code to follow these user interaction touch points: Firstly, the user will have to specify the location of the input point cloud file • Then the user will conduct point cloud formatting & subsampling as input preparation • Then the user will be prompted to select the training dataset (already classed) OR they will have the option to label manually 10-15% of the original dataset (similar to this - [login to view URL]) • Once the training data is added, the RF classifier performs the task • The outcome is visualised in 3D • The user moves onto a second level where they label more detailed features needed for classification • Once the training data is added, the RF classifier performs the task • The outcome is visualised in 3D • (these steps are repeated until the maximum level is reached) • Classes are saved as individual files and then instances of each class are also saved • Exported classes and segments are translated to .las/.rcp format • A prompt appears confirming that the process has been undertaken correctly & accuracy / F1 is confirmed We don’t mind if this full solution is packaged into a software package or simply kept as a python code. Nevertheless, a clear guide/manual will have to be provided with the code.