Analisis Tingkat Ketelitian Penggunaan Foto Udara Format Kecil (FUFK) untuk Estimasi Perhitungan Volume Galian dan Timbunan
Bibliographic record
Abstract
Abstrak Pembangunan infrastruktur dapat diwali dengan pemilihan lahan yang tersedia, pemilihan meliputi berbagai pertimbangan seperti lokasi, akses, harga, hingga kontur lahan. Kontur lahan berpengaruh terhadap banyaknya biaya penyiapan lahan yang berkaitan dengan galian dan timbunan. Kontur lahan dapat diketahui dengan melakukan pengukuran terestris menggunakan theodolite, total station, atau RTK/GNSS. Namun, Biaya dan waktu yang diperlukan untuk pengukuran terestris tidak efesien untuk digunakan saat studi awal pemilihan lahan. Teknologi Foto Udara Format Kecil (FUFK) menjadi salah satu yang sedang dikembangkan karena lebih efisien dari sisi waktu dan biaya. FUFK diolah menjadi sebuah data DEM melalui metode stereo-plotting sehingga didapat gambaran ukuran dan kontur lahan. Namun, hasil dari ekstraksi FUFK memiliki keterbatasan ketelitian sehingga perlu dianalisa lebih lanjut ketelitiannya. Studi ini dilakukan untuk menganalisa ketelitian hasil ekstraksi FUFK secara geometrik dan menghitung kesalahan peta kontur yang dihasilkan jika dibandingkan dengan pengukuran terestris untuk pekerjaan galian dan timbunan. Studi dilakukan pada 2 lokasi dengan 9 kali percobaan tinggi terbang dan overlap yang berbeda-beda. Hasil dari studi ini secara keseluruhan ketelitian peta yang dihasilkan memiliki nilai ketelitian geometrik horizontal CE90 0,400 hingga CE90 0,158 dan nilai ketelitian vertikal LE90 0,648 hingga LE90 0,223 dan perhitungan galian dan timbunan memiliki kesalahan absolut 3.39% hingga 14.21%. Kata-kata Kunci: Foto udara format kecil (FUFK), galian, kontur, timbunan. Abstract Infrastructure development can be initiated by the selection of available land, the selection includes various considerations such as location, access, price, to land contours. The contours of the land affect the many costs of land preparation related to cut and fill work. The contours can be known by taking theestris measurements using theodolite, total station, or RTK/GNSS. However, the cost and time required for the measurement of terestris are not efficient to use during land selection. Small Format Aerial Photography (SFAP) technology is one that is being developed because it is more efficient. SFAP is processed into a DEM data through stereo-plotting methods so that an overview of the size and contours of the land is obtained. However, the results of SAPF extraction have limitations in accuracy. This study was conducted to analyze the accuracy of SAPF extraction results. The study was conducted in 2 locations with 9 experiments with different flying heights and overlaps. The results of this study as a whole had a horizontal geometric accuracy value of CE90 0.400 to CE90 0.158 and a vertical accuracy value of LE90 0.648 to LE90 0.223 and calculations of cut and fill volumes had absolute errors of 3.39% to 14.21%. Keywords: Contours, cut, fill, small format aerial photography (SFAP).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".