New Algorithm Finds Emergency Runways Hidden in Aerial Images in Real Time

When an aircraft suffers a critical failure far from any airport, the difference between a survivable emergency landing and a catastrophe often comes down to seconds. Pilots must scan the ground below for a long, flat, obstacle-free stretch of terrain that can absorb a stricken airframe, and they must do so while managing an unfolding crisis in the cockpit. A new study published in the International Journal of Aeronautical and Space Sciences by Adil Illi, Khadija Bouzaachane, Salah El Hadaj and El Mahdi El Guarmah of Cadi Ayyad University in Marrakech, Morocco, describes an automated pipeline that can perform that search computationally, transforming high-resolution aerial imagery into precisely delineated emergency landing strips in real time.

Most previous research in this area has concentrated on identifying single landing coordinates for small unmanned aerial vehicles. A quadcopter losing a motor needs a spot of ground measured in meters; a general-purpose aircraft needs a corridor, an extended strip whose length, width and straightness must all be verified simultaneously. The Moroccan team identified this as a fundamental gap: no widely adopted method existed to move beyond isolated landing points and automatically detect and outline full viable landing strips from imagery alone. Their answer was to reformulate the entire problem as one of geometric optimization, layered on top of a modern deep learning segmentation engine.

The first stage of the pipeline tackles the question of what terrain is safe to land on at all. The researchers built a hybrid deep learning model that fuses two complementary architectures: U-Net, a convolutional neural network originally developed for biomedical image segmentation, and SegFormer, a Transformer-based model that captures long-range context across an image. Rather than forcing a choice between the two, the team combined them through an ensemble method, blending their outputs so that the local, detail-sensitive reasoning of the convolutional network reinforces the global scene understanding of the Transformer, and vice versa. The result is a robust binary segmentation mask that classifies every pixel of an aerial image as either safe-to-land terrain or unsafe ground.

Technically, the pairing is well motivated. U-Net’s encoder-decoder structure excels at preserving fine spatial boundaries, while SegFormer’s self-attention mechanism allows it to reason about relationships between distant regions of a scene, such as whether a seemingly clear field is bounded by trees, power lines or buildings. This architecture hybridization reflects a broader trend in semantic segmentation research, where convolutional and attention-based approaches are increasingly merged to capture both fine texture and scene-level semantics. On a custom dataset of aerial imagery of Moroccan terrain, previously assembled and pixel-wise labeled by the same group for exactly this application, the ensemble achieved a Mean Intersection-over-Union of 80.84 percent for the segmentation task, a strong score indicating substantial agreement between the model’s safe-terrain masks and ground-truth annotations.

The segmentation mask, however, is only raw material. A safe region shaped like an amoeba is useless to a descending aircraft; what matters is the largest rectangle of contiguous usable ground that can be inscribed within it. That is the second and arguably most original contribution of the study: a novel, efficient algorithm that finds the Longest Inscribed Rectangle, which the authors abbreviate as LNIR, within any binary mask. The rectangle that solves this geometric optimization problem corresponds directly to the optimal landing strip, because it captures the maximal continuous run of terrain that satisfies the shape constraint an actual aircraft approach demands.

Geometric optimization problems of this kind are notoriously difficult. Finding the largest rectangle inside an arbitrary polygonal region is a classic problem in computational geometry, and naive approaches scale poorly with image size. The LNIR algorithm is designed for efficiency, operating directly on the segmentation output and extracting the best-fit strip without exhaustive search. Crucially, the team also generalized the method to handle strips of arbitrary orientation. A viable field does not care about the axes of the image grid, so the algorithm iteratively rotates the search space, re-evaluating candidate rectangles at each angle until the orientation yielding the longest inscribed strip is found. This rotation strategy ensures the method works equally well for a runway aligned north-south, east-west or anywhere in between.

Real-world deployment demands more than accuracy in ideal conditions. Emergency landings do not wait for clear skies, so the researchers stress-tested the framework under simulated adverse weather, applying fog and rain degradations to their imagery and re-running the full pipeline. The system maintained high performance despite the visual corruption, demonstrating a robustness that is essential for any safety-critical application. Fog and rain reduce contrast, blur edges and shift color distributions, conditions that routinely break computer vision systems trained on clean data; the fact that this pipeline survives them suggests the ensemble segmentation approach learns features that are genuinely structural rather than merely textural artifacts of fair weather.

Equally important is speed. The entire pipeline, from raw aerial image to delineated landing strip, demonstrates real-time capability, meaning it can in principle keep pace with the continuously updating view from an aircraft-mounted camera or an unmanned scout vehicle. The researchers frame the work as a powerful automated tool for a critical aviation safety application, and they have made the ingredients available to the community: the dataset of pixel-wise labeled Moroccan emergency landing sites is hosted on Mendeley Data, and the code implementing the LNIR method is publicly accessible on GitHub, lowering the barrier for other groups to reproduce, benchmark and extend the approach.

The implications extend beyond the immediate use case. The authors position the LNIR algorithm as a versatile geometric optimization tool with potential for broader feature extraction from remote sensing data anywhere that elongated rectangular structures matter. Road and railway segment extraction, agricultural strip monitoring, solar farm siting, vegetation corridor analysis and pipeline inspection all reduce, at some level, to finding the best inscribed or aligned rectangle within a classified region, and an efficient, rotation-invariant solver for that problem is a reusable piece of scientific infrastructure. The work also arrives at a moment when hybrid CNN-Transformer segmentation models are proliferating across remote sensing, from urban scene parsing to geological structure detection, and the Moroccan study offers a concrete demonstration that such hybrids can be pushed all the way through to actionable geometric outputs rather than stopping at pixel labels.

There remain, of course, the usual caveats separating an academic pipeline from certified flight hardware. The evaluation relied on a custom dataset drawn from Moroccan terrain, and generalization to deserts, forests, snowfields or dense urban environments would require further validation. The segmentation accuracy of roughly 81 percent IoU, while impressive, leaves room for error in exactly the boundary regions that determine whether a strip is long enough. And any autonomous emergency system would eventually need to integrate additional sensing modalities such as LiDAR depth information, account for slope and surface bearing strength, and satisfy the exacting certification standards of aviation regulators. Still, the conceptual leap is clear and consequential: the study shows that the full chain from aerial photograph to quantified, oriented, optimal landing strip can be automated end to end, in real time, in bad weather. For a pilot gliding toward unfamiliar ground with failing systems, that chain could one day mean the difference between guessing and knowing where the aircraft can safely come to rest.

Subject of Research: Automated detection and geometric delineation of emergency landing strips in aerial imagery using hybrid deep learning segmentation and a longest inscribed rectangle optimization algorithm

Article Title: Automated Delineation of Viable Emergency Landing Strips in Aerial Imagery Using a Novel Geometric Optimization Algorithm

Article References: Illi, A., Bouzaachane, K., El Hadaj, S., & El Guarmah, E. M. (2026). Automated Delineation of Viable Emergency Landing Strips in Aerial Imagery Using a Novel Geometric Optimization Algorithm. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01278-5

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01278-5

Keywords: emergency landing, aerial imagery, deep learning, semantic segmentation, U-Net, SegFormer, computational geometry, geometric optimization, inscribed rectangle, remote sensing, aviation safety, UAV