LoRa technology provides new potentials for long range localization. Unlike previous works which attach a device to a person for active localization, this paper presents a device free fingerprinting localization system based on LoRa technology. The rationale of this work is that the person standing on different positions can induce different multipaths, and the challenge is to extract locations from receiving signals. Through a careful mathematical analysis, we observe the key factors that can characterize the location features and then propose a novel fingerprinting construction method leveraging frequency hopping to expand the locations’ resolution. Considering the trade-off between computation cost and localization accuracy, we establish a lightweight convolutional neural network model for position estimation. Extensive experiments have been conducted to evaluate our design, and results indicate that the fingerprinting construction approach can well express the location diversity and the designed model achieves meter-level localization in long-range indoor and underground environments.