The objective of this study is to present a geographic information system based method for detecting and quantifying boulders (area, length, width, elevation, and transport distance) that couples image processing techniques with spatial analysis tools. This study focuses on a delta within Jezero Crater on Mars, the site of the upcoming Mars 2020 rover mission. Understanding the distribution of boulders and their properties is significant since they are critical for understanding surficial geology, sedimentology, and erosional/depositional processes on Mars. Additionally, boulders and large clasts pose a challenge to the traversability of terrain for both rovers and future human missions. A high-resolution imaging science experiment image of the Jezero delta was processed using a filtering/masking algorithm, which combines mean filtering, brightness interval masking, and range kernel filtering. User-determined conservative (no false-positive boulder pixels) and liberal (no false-negative pixels) brightness thresholds were used to generate boulder polygons and their minimum-bounding geometry. Using conservative parameters, 443,126 boulder-like features were identified, of which 153,582 (35%) were relevant boulders. For these boulders, the cumulative fractional area versus boulder width relationship flattens out as predicted in prior work, suggesting that approach is accurate, and further is simpler than existing approaches, and can identify boulders as small as 50 cm in diameter. The algorithm, which can be customized to various degrees of boulder rounding and works on images with different brightness and contrast levels and illumination angles, is applicable to boulder fields on Earth as well as Mars and other solar system bodies.