I included the following variables iteratively.
## Ranger result
##
## Call:
## ranger(formula = as.factor(class) ~ ., data = dat_train)
##
## Type: Classification
## Number of trees: 500
## Sample size: 3708
## Number of independent variables: 20
## Mtry: 4
## Target node size: 1
## Variable importance mode: none
## Splitrule: gini
## OOB prediction error: 3.53 %
## [1] 0.9509434
## predicted
## true 0 1
## 0 2541 32
## 1 99 1036
In the end, my model detected about 97 percent of the powerlines in this area.